mental health in research methodology

• Systematic review
• Open access
• Published: 10 October 2019

An integrative review on methodological considerations in mental health research – design, sampling, data collection procedure and quality assurance

• Eric Badu   ORCID: orcid.org/0000-0002-0593-3550 1 ,
• Anthony Paul O’Brien 2 &
• Rebecca Mitchell 3  

Archives of Public Health volume  77 , Article number:  37 ( 2019 ) Cite this article

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Several typologies and guidelines are available to address the methodological and practical considerations required in mental health research. However, few studies have actually attempted to systematically identify and synthesise these considerations. This paper provides an integrative review that identifies and synthesises the available research evidence on mental health research methodological considerations.

A search of the published literature was conducted using EMBASE, Medline, PsycINFO, CINAHL, Web of Science, and Scopus. The search was limited to papers published in English for the timeframe 2000–2018. Using pre-defined inclusion and exclusion criteria, three reviewers independently screened the retrieved papers. A data extraction form was used to extract data from the included papers.

Of 27 papers meeting the inclusion criteria, 13 focused on qualitative research, 8 mixed methods and 6 papers focused on quantitative methodology. A total of 14 papers targeted global mental health research, with 2 papers each describing studies in Germany, Sweden and China. The review identified several methodological considerations relating to study design, methods, data collection, and quality assurance. Methodological issues regarding the study design included assembling team members, familiarisation and sharing information on the topic, and seeking the contribution of team members. Methodological considerations to facilitate data collection involved adequate preparation prior to fieldwork, appropriateness and adequacy of the sampling and data collection approach, selection of consumers, the social or cultural context, practical and organisational skills; and ethical and sensitivity issues.

The evidence confirms that studies on methodological considerations in conducting mental health research largely focus on qualitative studies in a transcultural setting, as well as recommendations derived from multi-site surveys. Mental health research should adequately consider the methodological issues around study design, sampling, data collection procedures and quality assurance in order to maintain the quality of data collection.

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In the past decades there has been considerable attention on research methods to facilitate studies in various academic fields, such as public health, education, humanities, behavioural and social sciences [ 1 , 2 , 3 , 4 ]. These research methodologies have generally focused on the two major research pillars known as quantitative or qualitative research. In recent years, researchers conducting mental health research appear to be either employing both qualitative and quantitative research methods separately, or mixed methods approaches to triangulate and validate findings [ 5 , 6 ].

A combination of study designs has been utilised to answer research questions associated with mental health services and consumer outcomes [ 7 , 8 ]. Study designs in the public health and clinical domains, for example, have largely focused on observational studies (non-interventional) and experimental research (interventional) [ 1 , 3 , 9 ]. Observational design in non-interventional research requires the investigator to simply observe, record, classify, count and analyse the data [ 1 , 2 , 10 ]. This design is different from the observational approaches used in social science research, which may involve observing (participant and non- participant) phenomena in the fieldwork [ 1 ]. Furthermore, the observational study has been categorized into five types, namely cross-sectional design, case-control studies, cohort studies, case report and case series studies [ 1 , 2 , 3 , 9 , 10 , 11 ]. The cross-sectional design is used to measure the occurrence of a condition at a one-time point, sometimes referred to as a prevalence study. This approach of conducting research is relatively quick and easy but does not permit a distinction between cause and effect [ 1 ]. Conversely, the case-control is a design that examines the relationship between an attribute and a disease by comparing those with and without the disease [ 1 , 2 , 12 ]. In addition, the case-control design is usually retrospective and aims to identify predictors of a particular outcome. This type of design is relevant when investigating rare or chronic diseases which may result from long-term exposure to particular risk factors [ 10 ]. Cohort studies measure the relationship between exposure to a factor and the probability of the occurrence of a disease [ 1 , 10 ]. In a case series design, medical records are reviewed for exposure to determinants of disease and outcomes. More importantly, case series and case reports are often used as preliminary research to provide information on key clinical issues [ 12 ].

The interventional study design describes a research approach that applies clinical care to evaluate treatment effects on outcomes [ 13 ]. Several previous studies have explained the various forms of experimental study design used in public health and clinical research [ 14 , 15 ]. In particular, experimental studies have been categorized into randomized controlled trials (RCTs), non-randomized controlled trials, and quasi-experimental designs [ 14 ]. The randomized trial is a comparative study where participants are randomly assigned to one of two groups. This research examines a comparison between a group receiving treatment and a control group receiving treatment as usual or receiving a placebo. Herein, the exposure to the intervention is determined by random allocation [ 16 , 17 ].

Recently, research methodologists have given considerable attention to the development of methodologies to conduct research in vulnerable populations. Vulnerable population research, such as with mental health consumers often involves considering the challenges associated with sampling (selecting marginalized participants), collecting data and analysing it, as well as research engagement. Consequently, several empirical studies have been undertaken to document the methodological issues and challenges in research involving marginalized populations. In particular, these studies largely addresses the typologies and practical guidelines for conducting empirical studies in mental health. Despite the increasing evidence, however, only a few studies have yet attempted to systematically identify and synthesise the methodological considerations in conducting mental health research from the perspective of consumers.

A preliminary search using the search engines Medline, Web of Science, Google Scholar, and Scopus Index and EMBASE identified only two reviews of mental health based research. Among these two papers, one focused on the various types of mixed methods used in mental health research [ 18 ], whilst the other paper, focused on the role of qualitative studies in mental health research involving mixed methods [ 19 ]. Even though the latter two studies attempted to systematically review mixed methods mental health research, this integrative review is unique, as it collectively synthesises the design, data collection, sampling, and quality assurance issues together, which has not been previously attempted.

This paper provides an integrative review addressing the available evidence on mental health research methodological considerations. The paper also synthesises evidence on the methods, study designs, data collection procedures, analyses and quality assurance measures. Identifying and synthesising evidence on the conduct of mental health research has relevance to clinicians and academic researchers where the evidence provides a guide regarding the methodological issues involved when conducting research in the mental health domain. Additionally, the synthesis can inform clinicians and academia about the gaps in the literature related to methodological considerations.

Methodology

An integrative review was conducted to synthesise the available evidence on mental health research methodological considerations. To guide the review, the World Health Organization (WHO) definition of mental health has been utilised. The WHO defines mental health as: “a state of well-being, in which the individual realises his or her own potentials, ability to cope with the normal stresses of life, functionality and work productivity, as well as the ability to contribute effectively in community life” [ 20 ]. The integrative review enabled the simultaneous inclusion of diverse methodologies (i.e., experimental and non-experimental research) and varied perspectives to fully understand a phenomenon of concern [ 21 , 22 ]. The review also uses diverse data sources to develop a holistic understanding of methodological considerations in mental health research. The methodology employed involves five stages: 1) problem identification (ensuring that the research question and purpose are clearly defined); 2) literature search (incorporating a comprehensive search strategy); 3) data evaluation; 4) data analysis (data reduction, display, comparison and conclusions) and; 5) presentation (synthesising findings in a model or theory and describing the implications for practice, policy and further research) [ 21 ].

Inclusion criteria

The integrative review focused on methodological issues in mental health research. This included core areas such as study design and methods, particularly qualitative, quantitative or both. The review targeted papers that addressed study design, sampling, data collection procedures, quality assurance and the data analysis process. More specifically, the included papers addressed methodological issues on empirical studies in mental health research. The methodological issues in this context are not limited to a particular mental illness. Studies that met the inclusion criteria were peer-reviewed articles published in the English Language, from January 2000 to July 2018.

Exclusion criteria

Articles that were excluded were based purely on general health services or clinical effectiveness of a particular intervention with no connection to mental health research. Articles were also excluded when it addresses non-methodological issues. Other general exclusion criteria were book chapters, conference abstracts, papers that present opinion, editorials, commentaries and clinical case reviews.

Search strategy and selection procedure

The search of published articles was conducted from six electronic databases, namely EMBASE, CINAHL (EBSCO), Web of Science, Scopus, PsycINFO and Medline. We developed a search strategy based on the recommended guidelines by the Joanna Briggs Institute (JBI) [ 23 ]. Specifically, a three-step search strategy was utilised to conduct the search for information (see Table  1 ). An initial limited search was conducted in Medline and Embase (see Table 1 ). We analysed the text words contained in the title and abstract and of the index terms from the initial search results [ 23 ]. A second search using all identified keywords and index terms was then repeated across all remaining five databases (see Table 1 ). Finally, the reference lists of all eligible studies were manually hand searched [ 23 ].

The selection of eligible articles adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 24 ] (see Fig.  1 ). Firstly, three authors independently screened the titles of articles that were retrieved and then approved those meeting the selection criteria. The authors reviewed all the titles and abstracts and agreed on those needing full-text screening. E.B (Eric Badu) conducted the initial screening of titles and abstracts. A.P.O’B (Anthony Paul O’Brien) and R.M (Rebecca Mitchell) conducted the second screening of titles and abstracts of all the identified papers. The authors (E.B, A.P.O’B and R.M) conducted full-text screening according to the inclusion and exclusion criteria.

Flow Chart of studies included in the review

Data management and extraction

The integrative review used Endnote ×8 to screen and handle duplicate references. A predefined data extraction form was developed to extract data from all included articles (see Additional file 1 ). The data extraction form was developed according to Joanna Briggs Institute (JBI) [ 23 ] and Cochrane [ 24 ] manuals, as well as the literature associated with concepts and methods in mental health research. The data extraction form was categorised into sub-sections, such as study details (citation, year of publication, author, contact details of lead author, and funder/sponsoring organisation, publication type), objective of the paper, primary subject area of the paper (study design, methods, sampling, data collection, data analysis, quality assurance). The data extraction form also had a section on additional information on methodological consideration, recommendations and other potential references. The authors extracted results of the included papers in numerical and textual format [ 23 ]. EB (Eric Badu) conducted the data extraction, A.P.O’B (Anthony Paul O’Brien) and R.M (Rebecca Mitchell), conducted the second review of the extracted data.

Data synthesis

Content analysis was used to synthesise the extracted data. The content analysis process involved several stages which involved noting patterns and themes, seeing plausibility, clustering, counting, making contrasts and comparisons, discerning common and unusual patterns, subsuming particulars into general, noting relations between variability, finding intervening factors and building a logical chain of evidence [ 21 ] (see Table  2 ).

Study characteristics

The integrative review identified a total of 491 records from all databases, after which 19 duplicates were removed. Out of this, 472 titles and abstracts were assessed for eligibility, after which 439 articles were excluded. Articles not meeting the inclusion criteria were excluded. Specifically, papers excluded were those that did not address methodological issues as well as papers addressing methodological consideration in other disciplines. A total of 33 full-text articles were assessed – 9 articles were further excluded, whilst an additional 3 articles were identified from reference lists. Overall, 27 articles were included in the final synthesis (see Fig. 1 ). Of the total included papers, 12 contained qualitative research, 9 were mixed methods (both qualitative and quantitative) and 6 papers focused on quantitative data. Conversely, a total of 14 papers targeted global mental health research and 2 papers each describing studies in Germany, Sweden and China. The papers addressed different methodological issues, such as study design, methods, data collection, and analysis as well as quality assurance (see Table  3 ).

Mixed methods design in mental health research

Mixed methods research is defined as a research process where the elements of qualitative and quantitative research are combined in the design, data collection, and its triangulation and validation [ 48 ]. The integrative review identified four sub-themes that describe mixed methods design in the context of mental health research. The sub-themes include the categories of mixed methods, their function, structure, process and further methodological considerations for mixed methods design. These sub-themes are explained as follows:

Categorizing mixed methods in mental health research

Four studies highlighted the categories of mixed methods design applicable to mental health research [ 18 , 19 , 43 , 48 ]. Generally, there are differences in the categories of mixed methods design, however, three distinct categories predominantly appear to cross cut in all studies. These categories are function, structure and process. Some studies further categorised mixed method design to include rationale, objectives, or purpose. For instance, Schoonenboom and Johnson [ 48 ] categorised mixed methods design into primary and secondary dimensions.

The function of mixed methods in mental health research

Six studies explain the function of conducting mixed methods design in mental health research. Two studies specifically recommended that mixed methods have the ability to provide a more robust understanding of services by expanding and strengthening the conclusions from the study [ 42 , 45 ]. More importantly, the use of both qualitative and quantitative methods have the ability to provide innovative solutions to important and complex problems, especially by addressing diversity and divergence [ 48 ]. The review identified five underlying functions of a mixed method design in mental health research which include achieving convergence, complementarity, expansion, development and sampling [ 18 , 19 , 43 ].

The use of mixed methods to achieve convergence aims to employ both qualitative and quantitative data to answer the same question, either through triangulation (to confirm the conclusions from each of the methods) or transformation (using qualitative techniques to transform quantitative data). Similarly, complementarity in mixed methods integrates both qualitative and quantitative methods to answer questions for the purpose of evaluation or elaboration [ 18 , 19 , 43 ]. Two papers recommend that qualitative methods are used to provide the depth of understanding, whilst the quantitative methods provide a breadth of understanding [ 18 , 43 ]. In mental health research, the qualitative data is often used to examine treatment processes, whilst the quantitative methods are used to examine treatment outcomes against quality care key performance targets.

Additionally, three papers indicated that expansion as a function of mixed methods uses one type of method to answer questions raised by the other type of method [ 18 , 19 , 43 ]. For instance, qualitative data is used to explain findings from quantitative analysis. Also, some studies highlight that development as a function of mixed methods aims to use one method to answer research questions, and use the findings to inform other methods to answer different research questions. A qualitative method, for example, is used to identify the content of items to be used in a quantitative study. This approach aims to use qualitative methods to create a conceptual framework for generating hypotheses to be tested by using a quantitative method [ 18 , 19 , 43 ]. Three papers suggested that using mixed methods for the purpose of sampling utilize one method (eg. quantitative) to identify a sample of participants to conduct research using other methods (eg. qualitative) [ 18 , 19 , 43 ]. For instance, quantitative data is sequentially utilized to identify potential participants to participate in a qualitative study and the vice versa.

Structure of mixed methods in mental health research

Five studies categorised the structure of conducting mixed methods in mental health research, into two broader concepts including simultaneous (concurrent) and sequential (see Table 3 ). In both categories, one method is regarded as primary and the other as secondary, although equal weight can be given to both methods [ 18 , 19 , 42 , 43 , 48 ]. Two studies suggested that the sequential design is a process where the data collection and analysis of one component (eg. quantitative) takes place after the data collection and analysis of the other component (eg qualitative). Herein, the data collection and analysis of one component (e.g. qualitative) may depend on the outcomes of the other component (e.g. quantitative) [ 43 , 48 ]. An earlier review suggested that the majority of contemporary studies in mental health research use a sequential design, with qualitative methods, more often preceding quantitative methods [ 18 ].

Alternatively, the concurrent design collects and analyses data of both components (e.g. quantitative and qualitative) simultaneously and independently. Palinkas, Horwitz [ 42 ] recommend that one component is used as secondary to the other component, or that both components are assigned equal priority. Such a mixed methods approach aims to provide a depth of understanding afforded by qualitative methods, with the breadth of understanding offered by the quantitative data to elaborate on the findings of one component or seek convergence through triangulation of the results. Schoonenboom and Johnson [ 48 ] recommended the use of capital letters for one component and lower case letters for another component in the same design to indicate that one component is primary and the other is secondary or supplemental.

Process of mixed methods in mental health research

Five papers highlighted the process for the use of mixed methods in mental health research [ 18 , 19 , 42 , 43 , 48 ]. The papers suggested three distinct processes or strategies for combining qualitative and quantitative data. These include merging or converging the two data sets, connecting the two datasets by having one build upon the other; and embedding one data set within the other [ 19 , 43 ]. The process of connecting occurs when the analysis of one dataset leads to the need for the other data set. For instance, in the situation where quantitative results lead to the subsequent collection and analysis of qualitative data [ 18 , 43 ]. A previous study suggested that most studies in mental health sought to connect the data sets. Similarly, the process of merging the datasets brings together two sets of data during the interpretation, or transforms one type of data into the other type, by combining the data into new variables [ 18 ]. The process of embedding data into mixed method designs in mental health uses one dataset to provide a supportive role to the other dataset [ 43 ].

Consideration for using mixed methods in mental health research

Three studies highlighted several factors that need to be considered when conducting mixed methods design in mental health research [ 18 , 19 , 45 ]. Accordingly, these factors include developing familiarity with the topic under investigation based on experience, willingness to share information on the topic [ 19 ], establishing early collaboration, willingness to negotiate emerging problems, seeking the contribution of team members, and soliciting third-party assistance to resolve any emerging problems [ 45 ]. Additionally, Palinkas, Horwitz [ 18 ] recommended that mixed methods in the context of mental health research are mostly applied in studies that assess needs of services, examine existing services, developing new or adapting existing services, evaluating services in randomised control trials, and examining service implementation.

Qualitative study in mental health research

This theme describes the various qualitative methods used in mental health research. The theme also addresses methodological considerations for using qualitative methods in mental health research. The key emerging issues are discussed below:

Considering qualitative components in conducting mental health research

Six studies recommended the use of qualitative methods in mental health research [ 19 , 26 , 28 , 32 , 36 , 44 ]. Two qualitative research paradigms were identified, including the interpretive and critical approach [ 32 ]. The interpretive methodologies predominantly explore the meaning of human experiences and actions, whilst the critical approach emphasises the social and historical origins and contexts of meaning [ 32 ]. Two studies suggested that the interpretive qualitative methods used in mental health research are ethnography, phenomenology and narrative approaches [ 32 , 36 ].

The ethnographic approach describes the everyday meaning of the phenomena within a societal and cultural context, for instance, the way phenomena or experience is contrasted within a community, or by collective members over time [ 32 ]. Alternatively, the phenomenological approach explores the claims and concerns of a subject with a speculative development of an interpretative account within their cultural and physical environments focusing on the lived experience [ 32 , 36 ].

Moreover, the critical qualitative approaches used in mental health research are predominantly emancipatory (for instance, socio-political traditions) and participatory action-based research. The emancipatory traditions recognise that knowledge is acquired through critical discourse and debate but are not seen as discovered by objective inquiry [ 32 ]. Alternatively, the participatory action based approach uses critical perspectives to engage key stakeholders as participants in the design and conduct of the research [ 32 ].

Some studies highlighted several reasons why qualitative methods are relevant to mental health research. In particular, qualitative methods are significant as they emphasise naturalistic inquiry and have a discovery-oriented approach [ 19 , 26 ]. Two studies suggested that qualitative methods are often relevant in the initial stages of research studies to understand specific issues such as behaviour, or symptoms of consumers of mental services [ 19 ]. Specifically, Palinkas [ 19 ] suggests that qualitative methods help to obtain initial pilot data, or when there is too little previous research or in the absence of a theory, such as provided in exploratory studies, or previously under-researched phenomena.

Three studies stressed that qualitative methods can help to better understand socially sensitive issues, such as exploring the solutions to overcome challenges in mental health clinical policies [ 19 , 28 , 44 ]. Consequently, Razafsha, Behforuzi [ 44 ] recommended that the natural holistic view of qualitative methods can help to understand the more recovery-oriented policy of mental health, rather than simply the treatment of symptoms. Similarly, the subjective experiences of consumers using qualitative approaches have been found useful to inform clinical policy development [ 28 ].

Sampling in mental health research

The theme explains the sampling approaches used in mental health research. The section also describes the methodological considerations when sampling participants for mental health research. The sub-themes emerging are explained in the following sections:

Sampling approaches (quantitative)

Some studies reviewed highlighted the sampling approaches previously used in mental health research [ 25 , 34 , 35 ]. Generally, all quantitative studies tend to use several probability sampling approaches, whilst qualitative studies used non-probability techniques. The quantitative mental health studies conducted at community and population level employ multi-stage sampling techniques usually involving systematic sampling, stratified and random sampling [ 25 , 34 ]. Similarly, quantitative studies that recruit consumers in the hospital setting employ consecutive sampling [ 35 ]. Two studies reviewed highlighted that the identification of consumers of mental health services for research is usually conducted by service providers. For instance, Korver, Quee [ 35 ] research used a consecutive sampling approach by identifying consumers through clinicians working in regional psychosis departments, or academic centres.

Sampling approaches (qualitative)

Seven studies suggested that the sampling procedures widely used in mental health research involving qualitative methods are non-probability techniques, which include purposive [ 19 , 28 , 32 , 42 , 46 ], snowballing [ 30 , 32 , 46 ] and theoretical sampling [ 31 , 32 ]. The purposive sampling identifies participants that possess relevant characteristics to answer a research question [ 28 ]. Purposive sampling can be used in a single case study, or for multiple cases. The purposive sampling used in mental health research is usually extreme, or deviant case sampling, criterion sampling, and maximum variation sampling [ 19 ]. Furthermore, it is advised when using purposive sampling in a multistage level study, that it should aim to begin with the broader picture to achieve variation, or dispersion, before moving to the more focused view that considers similarity, or central tendencies [ 42 ].

Two studies added that theoretical sampling involved sampling participants, situations and processes based on concepts on theoretical grounds and then using the findings to build theory, such as in a Grounded Theory study [ 31 , 32 ]. Some studies highlighted that snowball sampling is another strategy widely used in mental health research [ 30 , 32 , 46 ]. This is ascribed to the fact that people with mental illness are perceived as marginalised in research and practically hard-to-reach using conventional sampling [ 30 , 32 ]. Snowballing sampling involves asking the marginalised participants to recommend individuals who might have direct knowledge relevant to the study [ 30 , 32 , 46 ]. Although this approach is relevant, some studies advise the limited possibility of generalising the sample, because of the likelihood of selection bias [ 30 ].

Sampling consideration

Four studies in this section highlighted some of the sampling considerations in mental health research [ 30 , 31 , 32 , 46 ]. Generally, mental health research should consider the appropriateness and adequacy of sampling approach by applying attributes such as shared social, or cultural experiences, or shared concern related to the study [ 32 ], diversity and variety of participants [ 31 ], practical and organisational skills, as well as ethical and sensitivity issues [ 46 ]. Robinson [ 46 ] further suggested that sampling can be homogenous or heterogeneous depending on the research questions for the study. Achieving homogeneity in sampling should employ a variety of parameters, which include demographic, graphical, physical, psychological, or life history homogeneity [ 46 ]. Additionally, applying homogeneity in sampling can be influenced by theoretical and practical factors. Alternatively, some samples are intentionally selected based on heterogeneous factors [ 46 ].

Data collection in mental health research

This theme highlights the data collection methods used in mental health research. The theme is explained according to three sub-themes, which include approaches for collecting qualitative data, methodological considerations, as well as preparations for data collection. The sub-themes are as follows:

Approaches for collecting qualitative data

The studies reviewed recommended the approaches that are widely applied in collecting data in mental health research. The widely used qualitative data collection approaches in mental health research are focus group discussions (FGDs) [ 19 , 28 , 30 , 31 , 41 , 44 , 47 ], extended in-depth interviews [ 19 , 30 , 34 ], participant and non-participant observation [ 19 ], Delphi data collection, quasi-statistical techniques [ 19 ] and field notes [ 31 , 40 ]. Seven studies suggest that FGDs are widely used data collection approaches [ 19 , 28 , 30 , 31 , 41 , 44 , 47 ] because they are valuable in gathering information on consumers’ perspectives of services, especially regarding satisfaction, unmet/met service needs and the perceived impact of services [ 47 ]. Conversely, Ekblad and Baarnhielm [ 31 ] recommended that this approach is relevant to improve clinical understanding of the thoughts, emotions, meanings and attitudes towards mental health services.

Such data collection approaches are particularly relevant to consumers of mental health services, due to their low self-confidence and self-esteem [ 41 ]. The approach can help to understand specific terms, vocabulary, opinions and attitudes of consumers of mental health services, as well as their reasoning about personal distress and healing [ 31 ]. Similarly, the reliance on verbal rather than written communication helps to promote the participation of participants with serious and enduring mental health problems [ 31 , 41 ]. Although FGD has several important outcomes, there are some limitations that need critical consideration. Ekblad and Baarnhielm [ 31 ] for example suggest, that marginalised participants may not always feel free to talk about private issues regarding their condition at the group level mostly due to perceived stigma and group confidentiality.

Some studies reviewed recommended that attempting to capture comprehensive information and analysing group interactions in mental health research requires the research method to use field notes as a supplementary data source to help validate the FGDs [ 31 , 40 , 41 ]. The use of field notes in addition to FGDs essentially provides greater detail in the accounts of consumers’ subjective experiences. Furthermore, Montgomery and Bailey [ 40 ] suggest that field notes require observational sensitivity, and also require having specific content such as descriptive and interpretive data.

Three studies in this section suggested that in-depth interviews are used to collect data from consumers of mental health services [ 19 , 30 , 34 ]. This approach is particularly important to explore the behaviour, subjective experiences and psychological processes; opinions, and perceptions of mental health services. de Jong and Van Ommeren [ 30 ] recommend that in-depth interviews help to collect data on culturally marked disorders, their personal and interpersonal significance, patient and family explanatory models, individual and family coping styles, symptom symbols and protective mediators. Palinkas [ 19 ] also highlights that the structured narrative form of extended interviewing is the type of in-depth interview used in mental health research. This approach provides participants with the opportunity to describe the experience of living with an illness and seeking services that assist them.

Consideration for data collection

Six studies recommended consideration required in the data collection process [ 31 , 32 , 37 , 41 , 47 , 49 ]. Some studies highlighted that consumers of mental health services might refuse to participate in research due to several factors [ 37 ] like the severity of their illness, stigma and discrimination [ 41 ]. Subsequently, such issues are recommended to be addressed by building confidence and trust between the researcher and consumers [ 31 , 37 ]. This is a significant prerequisite, as it can sensitise and normalise the research process and aims with the participants prior to discussing their personal mental health issues. Similarly, some studies added that the researcher can gain the confidence of service providers who manage consumers of mental health services [ 41 , 47 ], seek ethical approval from the relevant committee(s) [ 41 , 47 ], meet and greet the consumers of mental health services before data collection, and arrange a mutually acceptable venue for the groups and possibly supply transport [ 41 ].

Two studies further suggested that the cultural and social differences of the participants need consideration [ 26 , 31 ]. These factors could influence the perception and interpretation of ethical issues in the research situation.

Additionally, two studies recommended the use of standardised assessment instruments for mental health research that involve quantitative data collection [ 33 , 49 ]. A recent survey suggested that measures to standardise the data collection approach can convert self-completion instruments to interviewer-completion instruments [ 49 ]. The interviewer can then read the items of the instruments to respondents and record their responses. The study further suggested the need to collect demographic and behavioural information about the participant(s).

Preparing for data collection

Eight studies highlighted the procedures involved in preparing for data collection in mental health research [ 25 , 30 , 33 , 34 , 35 , 39 , 41 , 49 ]. These studies suggest that the preparation process involve organising meetings of researchers, colleagues and representatives of the research population. The meeting of researchers generally involves training of interviewers about the overall design, objectives and research questions associated with the study. de Jong and Van Ommeren [ 30 ] recommended that preparation for the use of quantitative data encompasses translating and adapting instruments with the aim of achieving content, semantic, concept, criterion and technical equivalence.

Quality assurance procedures in mental health research

This section describes the quality assurance procedures used in mental health research. Quality assurance is explained according to three sub-themes: 1) seeking informed consent, 2) the procedure for ensuring quality assurance in a quantitative study and 3) the procedure for ensuring quality control in a qualitative study. The sub-themes are explained in the following content.

Seeking informed consent

The papers analysed for the integrative review suggested that the rights of participants to safeguard their integrity must always be respected, and so each potential subject must be adequately informed of the aims, methods, anticipated benefits and potential hazards of the study and any potential discomforts (see Table 3 ). Seven studies highlight that potential participants of mental health research must be consented to the study prior to data collection [ 25 , 26 , 33 , 35 , 37 , 39 , 47 ]. The consent process helps to assure participants of anonymity and confidentiality and further explain the research procedure to them. Baarnhielm and Ekblad [ 26 ] argue that the research should be guided by four basic moral values for medical ethics, autonomy, non-maleficence, beneficence, and justice. In particular, potential consumers of mental health services who may have severe conditions and unable to consent themselves are expected to have their consent signed by a respective family caregiver [ 37 ]. Latvala, Vuokila-Oikkonen [ 37 ] further suggested that researchers are responsible to agree on the criteria to determine the competency of potential participants in mental health research. The criteria are particularly relevant when potential participants have difficulties in understanding information due to their mental illness.

Procedure for ensuring quality control (quantitative)

Several studies highlighted procedures for ensuring quality control in mental health research (see Table 3 ). The quality control measures are used to achieve the highest reliability, validity and timeliness. Some studies demonstrate that ensuring quality control should consider factors such as pre-testing tools [ 25 , 49 ], minimising non-response rates [ 25 , 39 ] and monitoring of data collection processes [ 25 , 33 , 49 ].

Accordingly, two studies suggested that efforts should be made to re-approach participants who initially refuse to participate in the study. For instance, Liu, Huang [ 39 ] recommended that when a consumer of mental health services refuse to participate in a study (due to low self-esteem) when approached for the first time, a different interviewer can re-approach the same participant to see if they are more comfortable to participate after the first invitation. Three studies further recommend that monitoring data quality can be accomplished through “checks across individuals, completion status and checks across variables” [ 25 , 33 , 49 ]. For example, Alonso, Angermeyer [ 25 ] advocate that various checks are used to verify completion of the interview, and consistency across instruments against the standard procedure.

Procedure for ensuring quality control (qualitative)

Four studies highlighted the procedures for ensuring quality control of qualitative data in mental health research [ 19 , 32 , 37 , 46 ]. A further two studies suggested that the quality of qualitative research is governed by the principles of credibility, dependability, transferability, reflexivity, confirmability [ 19 , 32 ]. Some studies explain that the credibility or trustworthiness of qualitative research in mental health is determined by methodological and interpretive rigour of the phenomenon being investigated [ 32 , 37 ]. Consequently, Fossey, Harvey [ 32 ] propose that the methodological rigour for assessing the credibility of qualitative research are congruence, responsiveness or sensitivity to social context, appropriateness (importance and impact), adequacy and transparency. Similarly, interpretive rigour is classified as authenticity, coherence, reciprocity, typicality and permeability of the researcher’s intentions; including engagement and interpretation [ 32 ].

Robinson [ 46 ] explained that transparency (openness and honesty) is achieved if the research report explicitly addresses how the sampling, data collection, analysis, and presentation are met. In particular, efforts to address these methodological issues highlight the extent to which the criteria for quality profoundly interacts with standards for ethics. Similarly, responsiveness, or sensitivity, helps to situate or locate the study within a place, a time and a meaningful group [ 46 ]. The study should also consider the researcher’s background, location and connection to the study setting, particularly in the recruitment process. This is often described as role conflict or research bias.

In the interpretive phenomenon, coherence highlights the ability to select an appropriate sampling procedure that mutually matches the research aims, questions, data collection, analysis, as well as any theoretical concepts or frameworks [ 32 , 46 ]. Similarly, authenticity explains the appropriate representation of participants’ perspectives in the research process and the interpretation of results. Authenticity is maximised by providing evidence that participants are adequately represented in the interpretive process, or provided an opportunity to give feedback on the researcher’s interpretation [ 32 ]. Again, the contribution of the researcher’s perspective to the interpretation enhances permeability. Fossey, Harvey [ 32 ] further suggest that reflexive reporting, which distinguishes the participants’ voices from that of the researcher in the report, enhances the permeability of the researcher’s role and perspective.

One study highlighted the approaches used to ensure validity in qualitative research, which includes saturation, identification of deviant or non-confirmatory cases, member checking and coding by consensus. Saturation involves completeness in the research process, where all relevant data collection, codes and themes required to answer the phenomenon of inquiry are achieved; and no new data emerges [ 19 ]. Similarly, member checking is the process whereby participants or others who share similar characteristics review study findings to elaborate on confirming them [ 19 ]. The coding by consensus involves a collaborative approach to analysing the data. Ensuring regular meetings among coders to discuss procedures for assigning codes to segments of data and resolve differences in coding procedures, and by comparison of codes assigned on selected transcripts to calculate a percentage agreement or kappa measure of interrater reliability, are commonly applied [ 19 ].

Two studies recommend the need to acknowledge the importance of generalisability (transferability). This concept aims to provide sufficient information about the research setting, findings and interpretations for readers to appropriately determine the replicability of the findings from one context, or population to another, otherwise known as reliability in quantitative research [ 19 , 32 ]. Similarly, the researchers should employ reflexivity as a means of identifying and addressing potential biases in data collection and interpretation. Palinkas [ 19 ] suggests that such bias is associated with theoretical orientations; pre-conceived beliefs, assumptions, and demographic characteristics; and familiarity and experience with the methods and phenomenon. Another approach to enhance the rigour of analysis involves peer debriefing and support meetings held among team members which facilitate detailed auditing during data analysis [ 19 ].

The integrative review was conducted to synthesise evidence into recommended methodological considerations when conducting mental health research. The evidence from the review has been discussed according to five major themes: 1) mixed methods study in mental health research; 2) qualitative study in mental health research; 3) sampling in mental health research; 4) data collection in mental health research; and 5) quality assurance procedures in mental health research.

Mixed methods study in mental health research

The evidence suggests that mixed methods approach in mental health are generally categorised according to their function (rationale, objectives or purpose), structure and process [ 18 , 19 , 43 , 48 ]. The mixed methods study can be conducted for the purpose of achieving convergence, complementarity, expansion, development and sampling [ 18 , 19 , 43 ]. Researchers conducting mental health studies should understand the underlying functions or purpose of mixed methods. Similarly, mixed methods in mental health studies can be structured simultaneously (concurrent) and sequential [ 18 , 19 , 42 , 43 , 48 ]. More importantly, the process of combining qualitative and quantitative data can be achieved through merging or converging, connecting and embedding one data set within the other [ 18 , 19 , 42 , 43 , 48 ]. The evidence further recommends that researchers need to understand the stage of integrating the two sets of data and the rationale for doing so. This can inform researchers regarding the best stage and appropriate ways of combining the two components of data to adequately address the research question(s).

The evidence recommended some methodological consideration in the design of mixed methods projects in mental health [ 18 , 19 , 45 ]. These issues include establishing early collaboration, becoming familiar with the topic, sharing information on the topic, negotiating any emerging problems and seeking contributions from team members. The involvement of various expertise could ensure that methodological issues are clearly identified. However, addressing such issues midway, or late through the design can negatively affect the implementation [ 45 ]. Any robust discoveries can rarely be accommodated under the existing design. Therefore, the inclusion of various methodological expertise during inception can lead to a more robust mixed-methods design which maximises the contributions of team members. Whilst fundamental and philosophical differences in qualitative and quantitative methods may not be resolved, some workable solutions can be employed, particularly if challenges are viewed as philosophical rather than personal [ 45 ]. The cultural issues can be alleviated by understanding the concepts, norms and values of the setting, further to respecting and including perspectives of the various stakeholders.

The review findings suggest that qualitative methods are relevant when conducting mental health research. The qualitative methods are mostly used where there has been limited previous research and an absence of theoretical perspectives. The approach is also used to gather initial pilot data. More importantly, the qualitative methods are relevant when we want to understand sensitive issues, especially from consumers of mental health services, where the ‘lived experience is paramount [ 19 , 28 , 44 ]. Qualitative methods can help understand the experiences of consumers in the process of treatment, as well as their therapeutic relationship with mental health professionals. The experiences of consumers from qualitative data are particularly important in developing clinical policy [ 28 ]. The review findings find two paradigms of qualitative methods are used in mental health research. These paradigms are the interpretive and critical approach [ 32 ]. The interpretive qualitative method(s) include phenomenology, ethnography and narrative approaches [ 32 , 36 ]. Conversely, critical qualitative approaches are participatory action research and emancipatory approach. The review findings suggest that these approaches to qualitative methods need critical considerations, particularly when dealing with consumers of mental health services.

The review findings identified several sampling techniques used in mental health research. Quantitative studies, usually employ probability sampling, whilst qualitative studies use non-probability sampling [ 25 , 34 ]. The most common sampling techniques for quantitative studies are multi-stage sampling, which involves systematic, stratified, random sampling and consecutive sampling. In contrast, the predominant sampling approaches for qualitative studies are purposive [ 19 , 28 , 32 , 42 , 46 ], snowballing [ 30 , 32 , 46 ] and theoretical sampling [ 31 , 32 ].

The sampling of consumers of mental health services requires some important considerations. The sampling should consider the appropriateness and adequacy of the sampling approach, diversity and variety of consumers of services, attributes such as social, or cultural experiences, shared concerns related to the study, practical and organisational skills, as well as ethical and sensitivity issues are all relevant [ 31 , 32 , 46 ]. Sampling consumers of mental health services should also consider the homogeneity and heterogeneity of consumers. However, failure to address these considerations can present difficulty in sampling and subsequently result in selection and reporting bias in mental health research.

The evidence recommends several data collection approaches in collecting data in mental health research, including focus group discussion, extended in-depth interviews, observations, field notes, Delphi data collection and quasi-statistical techniques. The focus group discussions appear as an approach widely used to collect data from consumers of mental health services [ 19 , 28 , 30 , 31 , 41 , 44 , 47 ]. The focus group discussion appears to be a significant source of obtaining information. This approach promotes the participation of consumers with severe conditions, particularly at the group level interaction. Mental health researchers are encouraged to use this approach to collect data from consumers, in order to promote group level interaction. Additionally, field notes can be used to supplement information and to more deeply analyse the interactions of consumers of mental health services. Field notes are significant when wanting to gather detailed accounts about the subjective experiences of consumers of mental health services [ 40 ]. Field notes can help researchers to capture the gestures and opinions of consumers of mental health services which cannot be covered in the audio-tape recording. Particularly, the field note is relevant to complement the richness of information collected through focus group discussion from consumers of mental health services.

Furthermore, it was found that in-depth interviews can be used to explore specific mental health issues, particularly culturally marked disorders, their personal and interpersonal significance, patient and family explanatory models, individual and family coping styles, as well as symptom symbols and protective mediators [ 19 , 30 , 34 ]. The in-depth interviews are particularly relevant if the study is interested in the lived experiences of consumers without the contamination of others in a group situation. The in-depth interviews are relevant when consumers of mental health services are uncomfortable in disclosing their confidential information in front of others [ 31 ]. The lived experience in a phenomenological context preferably allows the consumer the opportunity to express themselves anonymously without any tacit coercion created by a group context.

The review findings recommend significant factors requiring consideration when collecting data in mental health research. These considerations include building confidence and trust between the researcher and consumers [ 31 , 37 ], gaining confidence of mental health professionals who manage consumers of mental health services, seeking ethical approval from the relevant committees, meeting consumers of services before data collection as well as arranging a mutually acceptable venue for the groups and providing transport services [ 41 , 47 ]. The evidence confirms that the identification of consumers of mental health services to participate in research can be facilitated by mental health professionals. Similarly, the cultural and social differences of the consumers of mental health services need consideration when collecting data from them [ 26 , 31 ].

Moreover, our review advocates that standardised assessment instruments can be used to collect data from consumers of mental health services, particularly in quantitative data. The self-completion instruments for collecting such information can be converted to interviewer-completion instruments [ 33 , 49 ]. The interviewer can read the questions to consumers of mental health services and record their responses. It is recommended that collecting data from consumers of mental health services requires significant preparation, such as training with co-investigators and representatives from consumers of mental health services [ 25 , 30 , 33 , 34 , 35 , 39 , 49 ]. The training helps interviewers and other investigators to understand the research project, particularly translating and adapting an instrument for the study setting with the aim to achieve content, semantic, concept, criteria and technical equivalence [ 30 ]. The evidence indicates that there is a need to adequately train interviewers when preparing for fieldwork to collect data from consumers of mental health services.

The evidence provides several approaches that can be employed to ensure quality assurance in mental health research involving quantitative methods. The quality assurance approach encompasses seeking informed consent from consumers of mental health services [ 26 , 37 ], pre-testing of tools [ 25 , 49 ], minimising non-response rates and monitoring of the data collection process [ 25 , 33 , 49 ]. The quality assurance process in mental health research primarily aims to achieve the highest reliability, validity and timeliness, to improve the quality of care provided. For instance, the informed consent exposes consumers of mental health services to the aim(s), methods, anticipated benefits and potential hazards and discomforts of participating in the study. Herein, consumers of mental health services who cannot respond to the inform consent process because of the severity of their illness can have it signed by their family caregivers. The implication is that researchers should determine which category of consumers of mental health services need family caregivers involved in the consent process [ 37 ].

The review findings advises that researchers should use pre-testing to evaluate the data collection procedure on a small scale and then to subsequently make any necessary changes [ 25 ]. The pre-testing aims to help the interviewers get acquainted with the procedures and to detect any potential problems [ 49 ]. The researchers can discuss the findings of the pre-testing and then further resolve any challenges that may arise prior to the actual field work being commenced. The non-response rates in mental health research can be minimised by re-approaching consumers of mental health services who initially refuse to participate in the study.

In addition, quality assurance for qualitative data can be ensured by applying the principles of credibility, dependability, transferability, reflexivity, confirmability [ 19 , 32 ]. It was found that the credibility of qualitative research in mental health is achieved through methodological and interpretive rigour [ 32 , 37 ]. The methodological rigour for assessing credibility relates to congruence, responsiveness or sensitivity to a social context, appropriateness, adequacy and transparency. By contrast, ensuring interpretive rigour is achieved through authenticity, coherence, reciprocity, typicality and permeability of researchers’ intentions, engagement and interpretation [ 32 , 46 ].

Strengths and limitations

The evidence has several strengths and limitations that require interpretation and explanation. Firstly, we employed a systematic approach involving five stages of problem identification, literature search, data evaluation, data synthesis and presentation of results [ 21 ]. Similarly, we searched six databases and developed a data extraction form to extract information. The rigorous process employed in this study, for instance, searching databases and data extraction forms, helped to capture comprehensive information on the subject.

The integrative review has several limitations largely related to the search words, language limitations, time period and appraisal of methodological quality of included papers. In particular, the differences in key terms and words concerning methodological issues in the context of mental health research across cultures and organisational contexts may possibly have missed some relevant articles pertaining to the study. Similarly, limiting included studies to only English language articles and those published from January 2000 to July 2018 could have missed useful articles published in other languages and those published prior to 2000. The review did not assess the methodological quality of included papers using a critical appraisal tool, however, the combination of clearly articulated search methods, consultation with the research librarian, and reviewing articles with methodological experts in mental health research helped to address the limitations.

The review identified several methodological issues that need critical attention when conducting mental health research. The evidence confirms that studies that addressed methodological considerations in conducting mental health research largely focuses on qualitative studies in a transcultural setting, in addition to lessons from multi-site surveys in mental health research. Specifically, the methodological issues related to the study design, sampling, data collection processes and quality assurance are critical to the research design chosen for any particular study. The review highlighted that researchers conducting mental health research can establish early collaboration, familiarise themselves with the topic, share information on the topic, negotiate to resolve any emerging problems and seek the contribution of clinical (or researcher) team members on the ground. In addition, the recruitment of consumers of mental health services should consider the appropriateness and adequacy of sampling approaches, diversity and variety of consumers of services, their social or cultural experiences, practical and organisational skills, as well as ethical and sensitivity issues.

The evidence confirms that in an attempt to effectively recruit and collect data from consumers of mental health services, there is the need to build confidence and trust between the researcher and consumers; and to gain the confidence of mental health service providers. Furthermore, seeking ethical approval from the relevant committee, meeting with consumers of services before data collection, arranging a mutually acceptable venue for the groups, and providing transport services, are all further important considerations. The review findings establish that researchers conducting mental health research should consider several quality assurance issues. Issues such as adequate training prior to data collection, seeking informed consent from consumers of mental health services, pre-testing of tools, minimising non-response rates and monitoring of the data collection process. More specifically, quality assurance for qualitative data can be achieved by applying the principles of credibility, dependability, transferability, reflexivity, confirmability.

Based on the findings from this review, it is recommended that mental health research should adequately consider the methodological issues regarding study design, sampling, data collection procedures and quality assurance issues to effectively conduct meaningful research.

Availability of data and materials

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Acknowledgements

The authors wish to thank the University of Newcastle Graduate Research and the School of Nursing and Midwifery, for the Doctoral Scholarship offered to the lead author. The authors are also grateful for the support received from Ms. Debbie Booth, the Librarian for supporting the literature search.

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EB, APO’B, and RM conceptualized the study. EB conducted the data extraction, APO’B, and RM, conducted the second review of the extracted data. EB, working closely with APO’B and RM performed the content analysis and drafted the manuscript. EB, APO’B, and RM, reviewed and made inputs into the intellectual content and agreed on its submission for publication. All authors read and approved the final manuscript.

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Badu, E., O’Brien, A.P. & Mitchell, R. An integrative review on methodological considerations in mental health research – design, sampling, data collection procedure and quality assurance. Arch Public Health 77 , 37 (2019). https://doi.org/10.1186/s13690-019-0363-z

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35 Qualitative research methods in mental health

• Published: July 2011
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In this chapter I set out to introduce the reader to qualitative research in mental health. I describe its main methods of inquiry, and its underpinning foundational and philosophical principles. I give numerous examples of where qualitative research has been fruitfully employed in community psychiatry. I then describe criteria of rigour which can be used to assess the strength and contribution of any qualitative study in mental health. I believe qualitative research continues to offer interesting insights into the prevention, diagnosis, phenomenology, treatment, management, and understanding of psychiatric disorder. It may also assist resolution of current policy imperatives, such as calls for person-centred care, and more thorough evaluations of service effectiveness.

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Qualitative Research in Mental Health and Mental Illness

Cite this chapter.

• Rebecca Gewurtz Ph.D. O.T. Reg. (Ont.) 6 ,
• Sandra Moll Ph.D. O.T. Reg. (Ont.) 6 ,
• Jennifer M. Poole M.S.W., Ph.D. 7 &
• Karen Rebeiro Gruhl Ph.D., M.Sc. O.T. OT. Reg. (Ont) 8  

Part of the book series: Handbooks in Health, Work, and Disability ((SHHDW,volume 4))

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In this chapter we present an overview of qualitative research in the mental health field. We provide an historical account of the vital role that qualitative methods have played in the development of theoretical and practice approaches of psychiatry, and their current use in contemporary mental health practice. We consider how different approaches to qualitative research are used to advance knowledge and understanding of mental health, mental illness, and related services and systems, as well as the contributions of qualitative research to the mental health field. We then provide a synthesis of evidence derived from qualitative research within the mental health sector, spanning four key areas: (1) recovery, (2) stigma, (3) employment, and (4) housing. We conclude this chapter with a review of the ongoing challenges facing qualitative researchers in this area.

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Qualitative Research

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Gewurtz, R., Moll, S., Poole, J.M., Gruhl, K.R. (2016). Qualitative Research in Mental Health and Mental Illness. In: Olson, K., Young, R., Schultz, I. (eds) Handbook of Qualitative Health Research for Evidence-Based Practice. Handbooks in Health, Work, and Disability, vol 4. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2920-7_13

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Complexity in Mental Health Research: Theory, Method, and Empirical Contributions

Guest edited by Dr Eiko I. Fried and Dr Donald Robinaugh

Mental disorders are dynamic, heterogeneous, and multicausal phenomena. Despite increasingly widespread recognition of this inherent complexity, progress in understanding mental disorders as complex biopsychosocial systems has been limited.

To advance our understanding of the etiology, prevention, and treatment of mental disorders, it is critical that both our theories and our research methods reflect the complex reality of psychopathology. In this collection, BMC Medicine will present a series of theoretical, methodological, and empirical papers that embrace complexity and chart a path forward for investigating mental disorders as complex systems.

We are seeking submissions in three domains:

• Empirical research. Example topics include causal relations among features of psychopathology, vicious cycles, emergence, attractor states of health and illness, phase transitions, early warning signals, resilience, adaptation, and bridging the gap between biological, psychological and social levels of analysis.
• Methodological contributions that either introduce newly developed methods for investigating mental disorders as complex systems; or that describe applications of methods drawn from other fields (network science, dynamic systems theory) to mental health research.
• Theoretical contributions that adopt a complex systems perspective, especially theories formalized as mathematical or computational models.

Importantly, while the subject of this collection is complexity, we are principally interested in contributions that can be readily understood by a broad audience, with implications not only for researchers, but also clinical practitioners, policy makers, and public health.

We welcome direct submission of original research within the article collection's scope. Please submit directly to BMC Medicine , indicating in your cover letter that you are targeting this collection. Alternatively, you can email a pre-submission query to the editorial team at [email protected] . The collection will remain open and accept submissions until July 2021.

Guest Editors provided guidance on the scope of this collection and advised on commissioned content. However, they are not involved in editorial decision-making on papers submitted to this collection. All final editorial decisions are with the Editor-in-Chief, Dr. Lin Lee.​

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Understanding the impact of exposure to adverse socioeconomic conditions on chronic stress from a complexity science perspective

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The importance of transdiagnostic symptom level assessment to understanding prognosis for depressed adults: analysis of data from six randomised control trials

Depression is commonly perceived as a single underlying disease with a number of potential treatment options. However, patients with major depression differ dramatically in their symptom presentation and comor...

Reducing youth suicide: systems modelling and simulation to guide targeted investments across the determinants

Reducing suicidal behaviour (SB) is a critical public health issue globally. The complex interplay of social determinants, service system factors, population demographics, and behavioural dynamics makes it ext...

Understanding personalized dynamics to inform precision medicine: a dynamic time warp analysis of 255 depressed inpatients

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Using person-specific networks in psychotherapy: challenges, limitations, and how we could use them anyway

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The Commentary to this article has been published in BMC Medicine 2020 18 :365

How important are parents in the development of child anxiety and depression? A genomic analysis of parent-offspring trios in the Norwegian Mother Father and Child Cohort Study (MoBa)

Many studies detect associations between parent behaviour and child symptoms of anxiety and depression. Despite knowledge that anxiety and depression are influenced by a complex interplay of genetic and enviro...

Early warning signals in psychopathology: what do they tell?

Despite the increasing understanding of factors that might underlie psychiatric disorders, prospectively detecting shifts from a healthy towards a symptomatic state has remained unattainable. A complex systems...

On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology

In the network approach to psychopathology, psychiatric disorders are considered networks of causally active symptoms (nodes), with node centrality hypothesized to reflect symptoms’ causal influence within a n...

Complexity in psychological self-ratings: implications for research and practice

Psychopathology research is changing focus from group-based “disease models” to a personalized approach inspired by complex systems theories. This approach, which has already produced novel and valuable insigh...

Comorbidity between depression and anxiety: assessing the role of bridge mental states in dynamic psychological networks

Comorbidity between depressive and anxiety disorders is common. A hypothesis of the network perspective on psychopathology is that comorbidity arises due to the interplay of symptoms shared by both disorders, ...

Towards formal models of psychopathological traits that explain symptom trajectories

A dominant methodology in contemporary clinical neuroscience is the use of dimensional self-report questionnaires to measure features such as psychological traits (e.g., trait anxiety) and states (e.g., depres...

Systems all the way down: embracing complexity in mental health research

In this editorial for the collection on complexity in mental health research, we introduce and summarize the inaugural contributions to this collection: a series of theoretical, methodological, and empirical p...

A complex systems approach to the study of change in psychotherapy

A growing body of research highlights the limitations of traditional methods for studying the process of change in psychotherapy. The science of complex systems offers a useful paradigm for studying patterns o...

Bridging the gap between complexity science and clinical practice by formalizing idiographic theories: a computational model of functional analysis

The past decades of research have seen an increase in statistical tools to explore the complex dynamics of mental health from patient data, yet the application of these tools in clinical practice remains uncom...

Mechanisms linking childhood trauma exposure and psychopathology: a transdiagnostic model of risk and resilience

Transdiagnostic processes confer risk for multiple types of psychopathology and explain the co-occurrence of different disorders. For this reason, transdiagnostic processes provide ideal targets for early inte...

Measuring resilience prospectively as the speed of affect recovery in daily life: a complex systems perspective on mental health

There is growing evidence that mental disorders behave like complex dynamic systems. Complex dynamic systems theory states that a slower recovery from small perturbations indicates a loss of resilience of a sy...

The complex neurobiology of resilient functioning after childhood maltreatment

Childhood maltreatment has been associated with significant impairment in social, emotional and behavioural functioning later in life. Nevertheless, some individuals who have experienced childhood maltreatment...

The Correction to this article has been published in BMC Medicine 2020 18 :202

Comparison of brain connectomes by MRI and genomics and its implication in Alzheimer’s disease

The human brain is complex and interconnected structurally. Brain connectome change is associated with Alzheimer’s disease (AD) and other neurodegenerative diseases. Genetics and genomics studies have identifi...

Unravelling the complex nature of resilience factors and their changes between early and later adolescence

Childhood adversity (CA) is strongly associated with mental health problems. Resilience factors (RFs) reduce mental health problems following CA. Yet, knowledge on the nature of RFs is scarce. Therefore, we ex...

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Many agree that the biopsychosocial contributions to psychopathology are complex, yet it is unclear how we can make sense of this complexity. One approach is to reduce this complexity to a few necessary and su...

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Qualitative and mixed methods in mental health services and implementation research

Affiliation.

• 1 a School of Social Work , University of Southern California.
• PMID: 25350675
• PMCID: PMC4212209
• DOI: 10.1080/15374416.2014.910791

Qualitative and mixed methods play a prominent role in mental health services research. However, the standards for their use are not always evident, especially for those not trained in such methods. This article reviews the rationale and common approaches to using qualitative and mixed methods in mental health services and implementation research based on a review of the articles included in this special series along with representative examples from the literature. Qualitative methods are used to provide a "thick description" or depth of understanding to complement breadth of understanding afforded by quantitative methods, elicit the perspective of those being studied, explore issues that have not been well studied, develop conceptual theories or test hypotheses, or evaluate the process of a phenomenon or intervention. Qualitative methods adhere to many of the same principles of scientific rigor as quantitative methods but often differ with respect to study design, data collection, and data analysis strategies. For instance, participants for qualitative studies are usually sampled purposefully rather than at random and the design usually reflects an iterative process alternating between data collection and analysis. The most common techniques for data collection are individual semistructured interviews, focus groups, document reviews, and participant observation. Strategies for analysis are usually inductive, based on principles of grounded theory or phenomenology. Qualitative methods are also used in combination with quantitative methods in mixed-method designs for convergence, complementarity, expansion, development, and sampling. Rigorously applied qualitative methods offer great potential in contributing to the scientific foundation of mental health services research.

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• Black Americans' perceptions of Alzheimer's disease, a healthy brain, and strategies for brain health promotion. Eliacin J, Polsinelli A, Cameron KA, Saykin AJ, Wang S. Eliacin J, et al. PEC Innov. 2024 Apr 19;4:100282. doi: 10.1016/j.pecinn.2024.100282. eCollection 2024 Dec. PEC Innov. 2024. PMID: 38706495 Free PMC article.
• Narrative Forewarnings: A Qualitative Analysis of the Themes Preceding Disorganized Speech in Schizophrenia. Bettis RJ, Faith LA, Beard AM, Whan BA, Hegwood CM, Monette MA, Myers EJ, Linton IS, Leonhardt BL, Salyers MP, Minor KS. Bettis RJ, et al. Behav Sci (Basel). 2024 Mar 6;14(3):212. doi: 10.3390/bs14030212. Behav Sci (Basel). 2024. PMID: 38540515 Free PMC article.
• Current methodologies of greenspace exposure and mental health research-a scoping review. Freymueller J, Schmid HL, Senkler B, Lopez Lumbi S, Zerbe S, Hornberg C, McCall T. Freymueller J, et al. Front Public Health. 2024 Mar 5;12:1360134. doi: 10.3389/fpubh.2024.1360134. eCollection 2024. Front Public Health. 2024. PMID: 38510363 Free PMC article.
• From theory to practice: challenges and rewards of implementing ketogenic metabolic therapy in mental health. Laurent N. Laurent N. Front Nutr. 2024 Feb 8;11:1331181. doi: 10.3389/fnut.2024.1331181. eCollection 2024. Front Nutr. 2024. PMID: 38389794 Free PMC article.
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• Bernard HR. Research methods in anthropology: Quantitative and qualitative approaches. 3rd. Walnut Creek, CA: Alta Mira Press; 2002.

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The Methods research area develops and applies innovative qualitative and quantitative methods for public mental health research, with a focus on statistical methods and economic models. These methods, applied across other program areas, are crucial for generating accurate answers to research questions. Faculty in the methods area address complications regarding missing data and non-adherence in randomized trials, as well as teach students how to analyze complex data such as DNA or complex longitudinal data, how to measure and model variables that are not directly observable and how to model the cost and benefit trade-offs of preventive interventions. There are strong links between the methods research area and other groups in the Department, such as the substance use research group, the Center for Prevention and Early Intervention and the Center for the Prevention of Youth Violence .

There are three particular research areas within the Methods area: causal inference, latent variables and measurement, and mixed methods.

• The causal inference area , led by Dr. Elizabeth Stuart, focuses on the development of statistical methods for estimating the effects of exposures, programs, or policies. This includes methods for non-experimental studies, such as estimating the long-term consequences of adolescent drug use or studying state opioid policies, as well as methods for designing and analyzing randomized experiments, such as of school-based preventive interventions, including work on mediation analysis within trials, and assessing the generalizability of trial results to target populations.
• A second area of focus, led by Dr. Rashelle Musci, surrounds measurement , including latent variables and measure harmonization. This includes the development and application of novel latent variable methods and data harmonization tools. With increasing availability of high quality extant data, methodology surrounding data harmonization is becoming increasingly important. The Department of Mental Health is on the front line of this research in regards to mental health with the harmonization of a variety of dataset types (ie., surveillance data, randomized controlled trial data, electronic medical records) for use in answering important questions related to mental health.
• A third area, led by Dr. Joseph Gallo, focuses on how to combine qualitative and quantitative methods, known as mixed methods . Mixed methods research is defined as the collection, analysis, and integration of both quantitative (e.g., RCT outcome) data and qualitative (e.g., observations, interviews) data to provide a more comprehensive understanding of a research problem than might be obtained through quantitative or qualitative research alone. Typical applications of mixed methods in the health sciences involve adding qualitative interviews to follow up on the outcomes of intervention trials, gathering both quantitative and qualitative data to assess patient reactions to a program implemented in a community health setting, or using qualitative data to explain the mechanism of a study correlating behavioral and social factors to specific health outcomes.

The Methods research area also has strong links with other departments and centers in the school. This includes joint appointments with the Department of Biostatistics, as well as links to methods-related groups such as the causal inference and health economics working groups. Student involvement in the Methods area consists of research assistance opportunities, as well as advising by faculty members in statistical and economic methods. Relevant coursework includes term-long and summer institute courses in the Department of Mental Health, such as the Methods seminar, courses in the design of cluster-randomized trials, and a two-term sequence on statistics for psychosocial research. Courses in the Biostatistics department are also relevant, including a causal inference course taught by Dr. Stuart. Many students interested in this program area also do a concurrent MHS in Biostatistics.

Faculty work on statistical methods for program and policy evaluation, helping assess the effectiveness of public health interventions. Specific methods questions include how well results from randomized trials carry over to target populations of policy interest, how to estimate the effects of exposures or interventions that can’t be randomized (e.g., childhood environmental exposures), and how to use longitudinal data to estimate the effects of state policies (e.g., opioid prescribing cap laws).

A group of faculty, staff, and students have been working on ways to measure mental health during the COVID-19 pandemic, including partnering with large-scale national and international surveys. Combining established measures of mental health with large-scale data collection activities, including the Understanding America Study out of the University of Southern California, and a Facebook-platform based survey administered through Carnegie Mellon University and the University of Maryland. Those collaborations and data collection efforts have enabled examination of patterns of mental distress during the pandemic in the US and internationally, and how mental distress relates to individual and area characteristics and policies.

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Center for mental health and addiction policy, alacrity center for health & longevity in mental illness, faculty spotlight.

Rashelle Jean Musci

Rashelle Musci, PhD, MS, is a methodologist and child mental health expert studying latent variable methods, intergenerational transmission, and impact of prevention programs.

Trang Q. Nguyen

Trang Quynh Nguyen, PhD '14, MHS '14, MS, uses causal inference methods to contribute to sound and effective research on physical and mental health and social justice.

Elizabeth A. Stuart

Elizabeth Stuart, PhD, uses statistical methods to help learn about the effects of public health programs and policies, often with a focus on mental health and substance use.

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Mixed-Methods Designs in Mental Health Services Research: A Review

• Lawrence A. Palinkas , Ph.D. ,
• Sarah M. Horwitz , Ph.D. ,
• Patricia Chamberlain , Ph.D. ,
• Michael S. Hurlburt , Ph.D. , and
• John Landsverk , Ph.D.

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Despite increased calls for use of mixed-methods designs in mental health services research, how and why such methods are being used and whether there are any consistent patterns that might indicate a consensus about how such methods can and should be used are unclear.

Use of mixed methods was examined in 50 peer-reviewed journal articles found by searching PubMed Central and 60 National Institutes of Health (NIH)-funded projects found by searching the CRISP database over five years (2005–2009). Studies were coded for aims and the rationale, structure, function, and process for using mixed methods.

A notable increase was observed in articles published and grants funded over the study period. However, most did not provide an explicit rationale for using mixed methods, and 74% gave priority to use of quantitative methods. Mixed methods were used to accomplish five distinct types of study aims (assess needs for services, examine existing services, develop new or adapt existing services, evaluate services in randomized controlled trials, and examine service implementation), with three categories of rationale, seven structural arrangements based on timing and weighting of methods, five functions of mixed methods, and three ways of linking quantitative and qualitative data. Each study aim was associated with a specific pattern of use of mixed methods, and four common patterns were identified.

Conclusions:

These studies offer guidance for continued progress in integrating qualitative and quantitative methods in mental health services research consistent with efforts by NIH and other funding agencies to promote their use. ( Psychiatric Services 62:255–263, 2011)

In the past decade, mental health services researchers have increasingly used qualitative methods in combination with quantitative methods ( 1 , 2 ). This use of mixed methods has been partly driven by theoretical models that encourage assessment of consumer perspectives and of contextual influences on disparities in the delivery of mental health services and the dissemination and implementation of evidence-based practices ( 3 , 4 ). These models call for research designs that use quantitative and qualitative data collection and analysis for a better understanding of a research problem than might be possible with use of either methodological approach alone ( 5 , 6 ). Numerous typologies and guidelines for the use of mixed-methods designs exist in the fields of nursing ( 7 , 8 ), evaluation ( 9 , 10 ), public health ( 11 , 12 ), primary care ( 13 ), education ( 14 ), and the social and behavioral sciences ( 5 , 15 ).

As Robins and colleagues ( 1 ) have observed, however, there has been little guidance in mental health services research on how to blend quantitative and qualitative methods to build upon the strengths of their respective epistemologies. Such guidance has been limited by the lack of consensus on the criteria that might be used to evaluate the quality of such research ( 5 ). From a policy perspective, the impact of the efforts of the National Institute of Mental Health (NIMH) ( 3 , 4 ) and other institutes of the National Institutes of Health (NIH) and funding agencies in encouraging the use of mixed methods in mental health services research also remains poorly understood.

To address these issues, we examined the application of mixed-methods designs in a sample of mental health services research studies published in peer-reviewed journals and in NIMH-funded research projects over five years. Our aim was to determine how and why such methods were being used and whether there are any consistent patterns that might indicate a consensus among researchers as to how such methods can and should be used. This aim is viewed as an initial step toward the development of standards for effective uses of mixed methods in mental health services research and articulation of criteria for evaluating the quality and impact of this research.

We conducted a literature review of mental health services research publications over a five-year period (January 2005 to September 2009), using the PubMed Central database and the following search terms: mental health services, mixed methods, and qualitative methods. Data were taken from the full text of each research article. Articles identified as potential candidates for inclusion had to report empirical research and meet one of the following selection criteria: a study specifically identified as a mixed-methods study in the title or abstract or through keywords; a qualitative study conducted as part of a larger project, including a randomized controlled trial, that also included use of quantitative methods; or a study that “quantitized” qualitative data ( 16 ) or “qualitized” quantitative data ( 17 ). On the basis of criteria used by McKibbon and Gadd ( 18 ) and Cresswell and Plano Clark ( 5 ), the analysis had to be fairly substantial; for example, a simple descriptive analysis of baseline demographic characteristics of participants was not sufficient to be included as a mixed-methods study. Further, qualitative studies that were not clearly linked to quantitative studies or methods were excluded from our review.

Using the same criteria and search terms, we also reviewed the NIH CRISP database (Computer Retrieval of Information on Scientific Projects) of projects funded over the same five-year period. Projects were limited to R series (independent research awards), F series (predissertation research awards), and K series (career development awards) grants. Data were taken from only the project descriptions provided by the applicant and contained in the database.

Using typologies employed in other fields of inquiry ( 5 – 7 , 9 ), we next assessed the use of mixed methods in each study to determine the study aims, rationale, structure, function, and process. Study aims referred to the objectives of the overall project that included both quantitative and qualitative studies or methods. The rationale for using mixed methods included conceptual reasons, such as exploration and confirmation ( 5 ), breadth and depth of understanding ( 19 ), and inductive and deductive theoretical drive ( 20 ). Pragmatic reasons for using mixed methods, such as addressing the weaknesses of one method by use of the other, and suitability to address research questions were also examined. Assessment of the structure of the research design was based on Morse's ( 7 ) taxonomy, which gives emphasis to timing (for example, using methods in sequence [represented by a → symbol] versus using them simultaneously [represented by a + symbol]) and to weighting (for example, primary method [represented by capital letters such as QUAN] versus secondary method [represented in lowercase letters such as qual]).

Assessment of the function of mixed methods was based on whether the two methods were being used to answer the same question or to answer related questions and whether they were used to achieve convergence, complementarity, expansion, development, or sampling ( 9 ). Finally, the process or strategies for combining qualitative and quantitative methods were assessed with the typology proposed by Cresswell and Plano Clark ( 5 ): merging or converging the two methods by actually bringing them together in the analysis or interpretation phase, connecting the two methods by having one build upon the results obtained by the other, or embedding one data set within the other so that one type of method provides a supportive role for the other method.

Our search identified 50 articles and 67 NIH-funded research projects published or funded between 2005 and 2009 that met our criteria for analysis. Seven of the NIH projects were excluded from further review because of missing data on the use of mixed methods. Three of the publications were based on one of the NIH-funded projects, and two other publications were based on one funded project each. Any redundant aims or strategies for combining qualitative and quantitative methods identified in linked publications and projects were counted only once in our analysis.

A list of the 26 journals in which the articles were published and the journals' impact factors (IFs) is presented in Table 1 . One-fifth of the articles were published in Psychiatric Services . The 2008 IFs of the journals for which information was available ranged from .74 ( Psychiatric Rehabilitation Journal ) to 4.84 ( Journal of the American Academy of Child and Adolescent Psychiatry ). Twenty-one of the 50 articles (42%) had an IF of 2.0 or greater. Of the funded grants, three were predissertation research grants (F31s), 28 were career development awards (K01, K08, K23, K24, and K99), and 29 were independent research awards (R01, R03, R18, R21, R24, and R34).

Table 2 presents the year of publication for the 50 articles and the start date of the 60 funded projects. Sixteen of the projects funded during this period had a start date before 2005. The smaller numbers of publications and of projects in 2009 reflect the shorter period of observation (nine months) for that year. There was an exponential increase in the number of publications between 2005 and 2008, and the number of grants from 2005 to 2009 was more than twice that of the previous five-year period (2000–2004).

Table 3 summarizes for comparison the use of mixed-methods designs on the basis of study aims. Our analyses revealed the use of mixed methods to accomplish five distinct types of study aims and three categories of rationale. We further identified seven structural arrangements, five uses or functions of mixed methods, and three ways of linking quantitative and qualitative data together. Some papers and projects included more than one objective, structure, or function; hence the raw numbers may occasionally sum to more than the total number of studies examined. Twelve of the 50 articles presented qualitative data only but were part of larger studies that included the use of quantitative measures. Further, we identified four commonly used designs, with each design associated with a specific aim or set of aims ( Figure 1 ).

As shown in Table 3 , the largest number of publications and projects (41 of 110, 37%) used mixed methods in observational or quasi-experimental studies of existing services. Almost one-quarter (24%) used mixed methods to study the implementation and dissemination of evidence-based practices. Mixed methods were also used to develop evidence-based practices, treatment, and interventions (17%); to conduct randomized controlled trials of interventions (14%); or to assess the needs of populations for mental health services (14%). Six studies had more than one aim (for example, two studies conducted a needs assessment before developing new interventions, and two studies examined implementation of an evidence-based practice within the context of a randomized controlled trial examining the practice's effectiveness.

Mixed-methods rationale

Forty-one of the 60 project abstracts (68%) and 25 of the 50 published articles (50%) did not provide an explicit rationale for the use of mixed methods; consequently, the rationale was inferred from statements found in project objectives. Of the 25 published articles that did provide an explicit rationale, only 11 provided one or more citations to justify use of mixed methods. The most common reason (93% of all articles and projects) for using mixed methods was based on the specific objectives of the study (for example, qualitative methods were needed for exploration or depth of understanding or quantitative methods were needed to test hypotheses). In other instances, use of mixed methods was dictated by the nature of the data; studies that included a focus on variables related to values and beliefs, the process of service delivery, or the context in which services are delivered relied on qualitative methods to describe and examine these phenomena. In 9% of articles and projects, investigators specifically indicated that both methods were used so that the strengths of one method could offset the weaknesses of the other ( Table 3 ).

Mixed-methods structure

The majority (58%) of the publications and projects used the methods in sequence, with qualitative methods more often preceding quantitative methods. Quantitative methods were the primary or dominant method in 74% of the publications and projects reviewed, and in 16 studies, qualitative and quantitative methods were given equal weight. In seven of the published studies, qualitative analyses were conducted on one or two open-ended questions attached to a survey, and 17 of the 50 published studies (34%) provided no references justifying their procedures for qualitative data collection or analysis. Only one published study ( 21 ) provided a figure that illustrated the timing and weighting of qualitative and quantitative data collection and analysis, and none used terms like QUAN and qual to describe this structure.

In studies that aimed to assess needs for mental health services, examine existing services, or develop new services or adapt existing services to new populations, sequential designs were used two to four times more frequently than simultaneous designs. The latter type of design was more commonly used in randomized controlled trials and in implementation studies.

Mixed-methods functions

Our review of the publications and projects revealed five distinct functions of mixing methods ( Table 3 ). The first function was convergence, in which qualitative and quantitative methods were used sequentially or simultaneously to answer the same question, either through triangulation (that is, the simultaneous use of one type of data to validate or confirm conclusions reached from analysis of the other type of data) or transformation (that is, the sequential quantification of qualitative data or use of qualitative techniques to transform quantitative data). For instance, Griswold and colleagues ( 22 ) triangulated quantitative trends in functional and health outcomes of psychiatric emergency department patients with qualitative findings of perceived benefits of care management and the value of integrated medical and mental health care to determine whether both types of data provided support for the effectiveness of a care management intervention (QUAN + QUAL). Using the technique of concept mapping ( 23 ), Aarons and colleagues ( 24 ) collected qualitative data on factors likely to have an impact on implementation of evidence-based practices in public-sector mental health settings. These data were then entered in a software program that uses multidimensional scaling and hierarchical cluster analysis to generate a visual display of statement clusters (QUAL → quan).

A second function of integrating quantitative and qualitative methods was complementarity, in which each method was used to answer related questions for the purpose of evaluation or elaboration. This function was evident in a majority (65%) of the published studies and projects examined. In evaluative designs, quantitative data were used to evaluate outcomes, whereas qualitative data were used to evaluate process. For instance, Bearsley-Smith and colleagues ( 25 ) described the use of quantitative methods to investigate the impact on clinical care of implementing interpersonal psychotherapy for adolescents within a rural mental health service and the use of qualitative methods to record the process and challenges (that is, feasibility, acceptability, and sustainability) associated with implementation and evaluation (QUAN + qual). In elaborative designs, qualitative methods were used to provide depth of understanding and quantitative methods were used to provide breadth of understanding. For instance, in a longitudinal study of mental health consumer-run organizations, Janzen and colleagues ( 26 ) used a quantitative tracking log for breadth of information about system-level activities and outcomes and key informant interviews and focus groups for greater insight into the impacts of these activities (QUAL + quan).

A third function of integrating qualitative and quantitative methods was expansion, in which one method was used in sequence to answer questions raised by the other method. This function was evident in 24% of the published studies and projects examined. In each instance, qualitative data were used to explain findings from the analyses of quantitative data. Brunette and colleagues ( 27 ) interviewed key informants and conducted ethnographic observations of implementation efforts to understand why some agencies adhered to established principles for integrated dual disorders treatment and others did not (QUAN + qual).

A fourth function of mixed methods was development, in which qualitative methods were used sequentially to identify form and content of items to be used in a quantitative study (for example, survey questions), to create a conceptual framework for generating hypotheses to be tested by using quantitative methods, or to develop new interventions or adapt existing interventions to new populations (qual → QUAN). This function was used in 34% of the published studies and projects. Blasinsky and colleagues ( 28 ) used qualitative findings from site visits to develop quantitative rating scales to construct predictors of outcomes and sustainability of a collaborative care intervention for older adults who had major depressive disorder or dysthymia. Green and colleagues ( 29 ) used qualitative data to generate a theoretical model of how relationships with clinics and clinicians' approach affect quality of life and recovery from serious mental illness and then tested the model using questionnaire data and health-plan and interview-based data in a covariance structure model. Several of the research projects funded through the R34 mechanism (for example, MH074509-01, Kilbourne, principal investigator [PI]; MH078583-01, Druss, PI; and MH073087-01, Lewis-Fernandez, PI) used qualitative data obtained from focus groups of consumers and providers to develop or adapt interventions for clients with specific conditions (for example, bipolar disorder, chronic medical conditions, and depressive disorders) (qual − QUAN).

The final function of mixed methods was sampling, the sequential use of one method to identify a sample of participants for research that uses the other method. This technique was used in only 7% of all studies. One form of sampling was the sequential use of quantitative data to identify potential participants for a qualitative study (quan − QUAL). For instance, Aarons and Palinkas ( 30 ) purposefully sampled candidates for qualitative interviews who had the most positive or most negative views of an evidence-based practice on the basis of a Web-based quantitative survey. The other form of sampling used qualitative data to identify samples of participants for quantitative analysis (qual − QUAN). Woltmann and colleagues ( 31 ) created categories of low, medium, and high staff turnover on the basis of staff perceptions of relevance of turnover obtained from qualitative interviews and then quantitatively examined the relationship between these turnover categories and implementation outcomes (qual + QUAN).

Only six of the published studies and none of the project abstracts explicitly referred to the function of mixed methods by using terms such as triangulation (four published studies) or complementarity (two published studies). As expected, the development function was used in a majority (84%) of studies that aimed to develop new practices or adapt existing practices to new populations. A majority of observational and quasi-experimental studies of existing services (71%), randomized controlled trials (67%), implementation studies (65%), and needs assessment studies (60%) utilized mixed methods for the purposes of answering related questions in complementary fashion. The use of one set of methods to explain the results of a study using another set of methods appears to have been limited to implementation studies (46%), randomized controlled trial evaluations (40%), and studies of existing services (20%).

Process of mixing methods

The final characteristic of mixed-methods designs that we examined was the process of mixing the quantitative and qualitative methods. The largest percentage (47%) of articles and projects sought to connect the data sets ( Table 3 ). This occurs when the analysis of one data set leads to (and thereby connects to) the need for the other data set, such as when quantitative results lead to the subsequent collection and analysis of qualitative data (that is, expansion) or when qualitative results are used to build to the subsequent collection and analysis of quantitative data, (for example, development) ( 5 ). For instance, Frueh and colleagues ( 32 ) conducted focus groups to obtain information on the target population, their providers, and state-funded mental health systems that would enable the researchers to further adapt and improve a cognitive-behavioral therapy-based intervention for treatment of posttraumatic stress disorder before implementing it (qual → QUAN). This type of mixing was found in almost all of the studies with aims to develop new practices or adapt existing practices to new populations; it was also more likely to be found in needs assessment and studies of existing services than in randomized controlled trials or implementation studies.

Over one-third (37%) of the studies merged the knowledge gained from the quantitative and qualitative data, either during the interpretation phase when two sets of results that had been analyzed separately were brought together or during the analysis phase when one type of data was transformed into the other type by consolidating the data into new variables ( 5 ). This type of mixing was found in slightly less than half of the needs assessment, observational, and implementation studies. For instance, Lucksted and colleagues ( 33 ) reported that a qualitative analysis of responses to an open-ended postintervention question supported the quantitative findings of the benefits of a relapse prevention and wellness program (QUAN + qual).

The embedding of small qualitative or qualitative-quantitative studies within larger quantitative studies was observed in 35% of the published studies and projects reviewed and described as “nested designs” in six of the studies. This type of mixing was more commonly found in randomized controlled trials and in implementation studies, where qualitative studies of treatment or implementation process or context were embedded within larger quantitative studies of treatment or implementation outcome. For instance, to better understand the essential components of the patient-provider relationship in a public health setting, Sajatovic and colleagues ( 34 ) conducted a qualitative investigation of patients' attitudes toward a collaborative care model and how individuals with bipolar disorder perceive treatment adherence within the context of a randomized controlled trial evaluating a collaborative practice model (QUAN + qual).

In 20% of published studies, more than one process was evident. For instance, Proctor and colleagues ( 35 ) connected the data by generating frequencies and rankings of qualitative data on perceptions of competing psychosocial problems collected from a community sample of 49 clients with a history of depression. These data were then merged with quantitative measures of depression status obtained through administration of the Patient Health Questionnaire-9 to explore the relationship of depression severity to problem categories and ranks.

The results of our analysis indicate that there has been substantial progress in using mixed-methods designs in mental health services research in response to efforts by NIMH ( 2 , 3 ) and other funding agencies to promote their use. Evidence for this progress is found in the increasing number of research projects that use mixed methods. The number of projects with mixed-methods designs funded over the five-year study period was more than twice the number that began in the previous five-year period (2000–2004). Furthermore, a majority (52%) of these funded projects were predissertation or career development awards used by junior and midlevel investigators to acquire expertise in mixed-methods research.

We also observed a notable increase in the number of studies based on mixed-methods designs published each year during this five-year period. The number of published mental health services research studies with mixed-methods designs increased by 67% between 2005 and 2006, by 80% between 2006 and 2007, and by 155% between 2007 and 2008. Furthermore, 21 of the 50 published studies (42%) that we reviewed appeared in journals with 2008 IFs of 2.0 or higher, including ten articles published in Psychiatric Services; four articles appeared in a journal with an IF of 4.0 or higher. In contrast, McKibbon and Gadd ( 18 ) reported that only 11 of 37 (30%) mixed-methods studies of health services appeared in a journal with an IF of 2.0 or higher in the year 2000.

Despite this progress, however, our review also suggests that there is room for improvement in use of mixed-methods designs. Most studies did not make explicit or provide support for the reasons for choosing a mixed-methods design; rather, we were forced to infer the rationale based on statements explaining what the methods were used for. Researchers may have felt that such explicit statements were as unnecessary as statements explaining the rationale for using certain quantitative methods, such as analysis of variance or survival analysis. However, the absence of an explicit rationale may also reflect a lack of understanding or appreciation of mixed-methods designs or a decision to use them without necessarily integrating or “mixing” them ( 5 , 6 ).

Most studies failed to provide explicit descriptions of the design structure or function that used terminology found in the mixed-methods literature; use of such terminology is consistent with the general standards for high-quality mixed-methods research recommended by Cresswell and Plano Clark ( 5 ). Further, three-fourths of the 50 published studies reviewed assigned priority to the use of quantitative methods, seven of the studies performed qualitative analyses of one or two open-ended questions attached to a survey, and 17 of the studies provided no references justifying their procedures for qualitative data collection or analysis. This may reflect an underappreciation of qualitative methods, as Robins and colleagues ( 1 ) have argued, or it may reflect a greater need for quantitative methods at the present time.

Although it was beyond the scope of this review to determine whether each study used mixed methods in effective ways, we note that each study was subjected to rigorous peer review before being published or funded, and each was judged by this process to make a valuable contribution to the field of mental health services research. These studies also provide evidence of meaningful and sensible variations in mixed-methods approaches to achieving various kinds of study aims and offer some guidance for integrating quantitative and qualitative methods in mental health services research. For instance, the choice of a mixed-methods design appears to be dictated by the nature of the questions being asked by mental health services researchers. Qualitative methods were used to explore a phenomenon when there was little or no previous research or to examine that phenomenon in depth, whereas quantitative methods were used to confirm hypotheses or examine the generalizability of the phenomenon and its associated predictors.

A majority of studies aiming to develop new practices or adapt existing practices to new populations had the same structure (beginning with a small qualitative study before developing or adapting the practice that was to be evaluated by using quantitative methods, which was found in 84% of the studies and projects) and the same process (connecting the findings of one set of methods with those of another set, which was found in 90% of the studies and projects). These studies reflect a growing awareness of the need to incorporate the preferences and perspectives of both service consumers and providers to ensure that new practices will be acceptable as well as feasible ( 32 , 36 – 39 ).

Studies of existing services also tended to be sequential in structure, with qualitative methods used to elaborate or explain the findings of quantitative studies. In the majority of these studies, the process of mixing methods involved either merging two sets of data to achieve convergence or connecting them to achieve expansion ( 5 ). A similar pattern was observed in studies that aimed to explore issues related to the needs for mental health services or provide more depth to our understanding of those needs. Such studies also appeared more likely to transform or “quantitize” qualitative data ( 24 , 35 ).

Randomized controlled trials and studies of implementation also shared similar patterns in use of mixed methods, including simultaneous use of both methods to achieve complementarity by embedding a qualitative or qualitative-quantitative study within a larger quantitative study, such as a randomized controlled trial. In the randomized controlled trials, qualitative methods were usually used to evaluate the process of providing the practice or intervention, whereas quantitative methods were used to evaluate the outcomes ( 25 , 40 ). In implementation research studies, qualitative methods were used to explore or provide depth to understanding barriers and facilitators of intervention implementation, whereas quantitative methods were used to confirm hypotheses and provide breadth to understanding by assessing the generalizability of findings ( 41 , 42 ).

The choice of mixed-methods designs was also dictated by how the individual questions being addressed by each method were related to one another. Studies that used different types of data to answer the same question reflected the function of convergence in a simultaneous structure, where data were merged for the purpose of triangulation, or a sequential structure, where qualitative data were transformed into quantitative data. Studies that used different types of data to answer related questions reflected the function of complementarity, in which quantitative methods were used to measure outcomes, describe content (for example, fidelity of services used and the nature of the mental health problem), and provide breadth (generalizability) of understanding, whereas qualitative methods were used to evaluate the process of service delivery ( 43 – 45 ), describe context (for example, setting) ( 26 , 34 , 46 ), describe consumer values or attitudes ( 35 , 42 , 47 ), and provide depth (meaning) of understanding ( 28 , 48 ) in a simultaneous structure and embedded data process. Expansion, development, and sampling were also used to provide answers to related questions that could not be answered by one method alone, usually in a sequential structure in which data sets were merged or connected together ( 24 , 30 , 37 ).

Finally, the choice of design appears to be based on the strengths of one method relative to the weaknesses of the other. For instance, expansion was used to explain findings based on quantitative data with qualitative data because explanation was not possible with the quantitative methods alone ( 25 , 27 , 40 ). In convergence, both sets of methods were used to confirm or validate one another, especially in instances where limited samples precluded testing of hypotheses with sufficient statistical power ( 30 , 49 ) and where limitations to qualitative data collection raised concerns about objectivity and transferability of results. In studies developing new methods, conceptual models, and interventions, qualitative methods also served to enhance quantitative analysis by laying the groundwork essential for more valid measurement and theory and more effective, usable, and sustainable interventions ( 37 ). Sampling also worked to enhance validity by using qualitative methods to enhance quantitative methods by developing targeted comparisons or by using quantitative methods to enhance qualitative methods by establishing criteria for purposeful sampling ( 36 ).

In summary, the choice of a mixed-methods design appears to be associated with three considerations: the nature of the question being asked (inductive-exploratory or deductive-confirmatory), how the questions being addressed by each method are related to one another, and the strengths of each method relative to the weaknesses of the other.

Caution should be exercised in interpreting these findings given limitations in our study design and analysis. Despite our efforts to be comprehensive in the search process and to select studies and projects on the basis of criteria with face validity, we undoubtedly excluded several articles or projects that used mixed methods. For example, we may have excluded mixed-methods projects listed in the CRISP database that did not specify use of qualitative or mixed methods in the abstracts. We may have also excluded published articles with qualitative data that were part of larger, primarily quantitative studies if the articles did not reference the larger studies, or we may have excluded articles not listed in PubMed Central. In the absence of explicit information, we were often forced to infer the structure, rationale, and function of the design based on statements contained in the available material. Similarly, the CRISP abstracts describe only what the investigators proposed to do with mixed methods and do not indicate what was actually done. Our use of existing typologies of structure, function, and process were intended to serve as a starting point in our analysis rather than an attempt to “pigeon-hole” each study into a specific typology. Our assessment of the progress made in the application of mixed-methods designs in response to calls for their use by funding agencies did not include indicators of whether these efforts had produced more useful, incisive, or insightful knowledge for the purpose of addressing mental health services questions and problems. Such an assessment would require comparisons with the products of studies based on monomethod designs, which was beyond the scope of this study.

Finally, it should be noted that the typology of mixed-methods use does not represent a set of standards for using mixed methods per se but is an important first step toward the development of such standards. Typologies by themselves do not explain why a particular method should be used and how to use a method appropriately. However, as Teddlie and Tashakkori ( 6 ) observed, there are five reasons or benefits to developing such a typology: typologies help to provide the field with an organizational structure, they provide examples of research designs that are clearly distinct from either qualitative or quantitative research designs, they help to establish a common language for the field, they help researchers decide how to proceed when designing their studies, and they are useful as a pedagogical tool. A consensus conference or workshop bringing together experts in mixed methods and mental health services research to evaluate the empirically generated typology found in current patterns of mixed-methods use would appear to be the next logical step in developing a set of standards. Such standards would also be required to adhere to the epistemological foundations of each method when used separately (for example, whether appropriate considerations are made to ensure the generalizability of quantitative results or theoretical saturation of qualitative data and whether each method is appropriately matched to the inductive or deductive theoretical drive of the study) and when combined (for example, whether the knowledge gained when using the two methods together is more insightful and of greater value than the knowledge gained when using them separately).

Conclusions

Despite the limitations described above, the findings suggest an increasing use of mixed-methods designs to address changing priorities in mental health services research and a consensus as to how such methods should be applied. The lack of explicit statements explaining the rationale for using mixed methods and the evident priority assigned to quantitative methods suggest that there is room for improvement. However, these studies appear to utilize a common set of designs and provide guidance for using mixed methods, with varying approaches based on the nature of the question being asked (exploratory or confirmatory), how questions being addressed by each method are related to one another, and the strengths of each method relative to the weaknesses of the other.

Acknowledgments and disclosures

This study was funded through NIMH grant P50-MH50313-07.

The authors report no competing interests.

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Figures and Tables

Figure 1 Common mixed-methods designs used in mental health services research

Table 1 Journals in which the 50 articles reviewed were published, with number published and 2008 impact factor

Table 2 Year of publication or of project initiation of articles and projects reviewed

Table 3 Characteristics of 50 published studies and 60 funded projects that used mixed-methods designs, by study aims

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• A Community-Partnered Research Process for Implementation Strategy Design: Developing Resources to Support Behavioral Classroom Interventions 18 June 2024 | School Mental Health, Vol. 38
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• Mixed method study of workforce turnover and evidence-based treatment implementation in community behavioral health care settings Child Abuse & Neglect, Vol. 102
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• SIPsmartER delivered through rural, local health districts: adoption and implementation outcomes 18 September 2019 | BMC Public Health, Vol. 19, No. 1
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• Five Challenges in the Design and Conduct of IS Trials for HIV Prevention and Treatment JAIDS Journal of Acquired Immune Deficiency Syndromes, Vol. 82, No. 3
• Mental health recovery narratives: their impact on service users and other stakeholder groups Mental Health and Social Inclusion, Vol. 23, No. 4
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• American Journal of Community Psychology
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• Journal of Public Child Welfare, Vol. 13, No. 3
• Method Sequence and Dominance in Mixed Methods Research: A Case Study of the Social Acceptance of Wind Energy Literature 12 April 2019 | International Journal of Qualitative Methods, Vol. 18
• JMIR Research Protocols, Vol. 8, No. 1
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• Nursing Outlook, Vol. 66, No. 2
• Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen, Vol. 133
• Social Work in Mental Health, Vol. 16, No. 4
• International Journal of Family & Community Medicine, Vol. 2, No. 4
• A mixed-methods study of system-level sustainability of evidence-based practices in 12 large-scale implementation initiatives 7 December 2017 | Health Research Policy and Systems, Vol. 15, No. 1
• Fostering Psychotropic Medication Oversight for Children in Foster Care: A National Examination of States’ Monitoring Mechanisms 10 February 2016 | Administration and Policy in Mental Health and Mental Health Services Research, Vol. 44, No. 2
• Beliefs and Behaviors of Pregnant Women with Addictions Awaiting Treatment Initiation 17 November 2016 | Child and Adolescent Social Work Journal, Vol. 34, No. 1
• Psychiatric Quarterly, Vol. 88, No. 3
• Quality & Quantity, Vol. 51, No. 1
• Translational Behavioral Medicine, Vol. 7, No. 3
• Psychology, Health & Medicine, Vol. 22, No. 5
• Changes in Social Networks and HIV Risk Behaviors Among Homeless Adults Transitioning Into Permanent Supportive Housing 8 July 2016 | Journal of Mixed Methods Research, Vol. 11, No. 1
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• Impact of Caregiver Factors on Youth Service Utilization of Trauma-Focused Cognitive Behavioral Therapy in a Community Setting 27 February 2016 | Journal of Child and Family Studies, Vol. 25, No. 6
• Measuring Current Drug Use in Female Sex Workers and Their Noncommercial Male Partners in Mexico: Concordance Between Data Collected From Surveys Versus Semi-Structured Interviews 18 December 2015 | Substance Use & Misuse, Vol. 51, No. 1
• Administration and Policy in Mental Health and Mental Health Services Research, Vol. 43, No. 4
• Implementation and Outcomes of Forensic Housing First Programs 5 October 2015 | Community Mental Health Journal, Vol. 52, No. 1
• Academic Psychiatry, Vol. 40, No. 4
• Journal of Psychoactive Drugs, Vol. 48, No. 5
• Rural Society, Vol. 25, No. 2
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• Technical Efficiency and Organ Transplant Performance: A Mixed-Method Approach 5 May 2015 | International Journal of Environmental Research and Public Health, Vol. 12, No. 5
• Perceptions of clinicians treating young people with first‐episode psychosis for post‐traumatic stress disorder 27 June 2013 | Early Intervention in Psychiatry, Vol. 9, No. 1
• Administration and Policy in Mental Health and Mental Health Services Research, Vol. 42, No. 2
• Administration and Policy in Mental Health and Mental Health Services Research, Vol. 42, No. 5
• Journal of Religion and Health, Vol. 54, No. 1
• The Journal of Behavioral Health Services & Research, Vol. 42, No. 4
• BMC Palliative Care, Vol. 14, No. 1
• Implementation Science, Vol. 11, No. 1
• Evidence-Based Programs in “Real World” Settings: Finding the Best Fit
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• Lawrence A. Palinkas , Ph.D.
• Causality and Causal Inference in Social Work 22 May 2014 | Research on Social Work Practice, Vol. 24, No. 5
• A Systematic Review of Strategies for Implementing Empirically Supported Mental Health Interventions 8 October 2013 | Research on Social Work Practice, Vol. 24, No. 2
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Answering questions about mental health: Which approach to take?

Blog by Alumni Fellows Jorien Treur, Margot van de Weijer, Robyn Wootton

17 June 2024

All three of us (Jorien Treur, Robyn Wootton, Margot van de Weijer) come from a field called “genetic epidemiology”, in which we focus on how specific genetic and environmental factors (and the interaction between them) contribute to health and behaviour. We came to the fellowship aware of the different biases and limitations inherent to methods used in our field, and were interested in how we could combine different methods in a way that helps us answer causal questions, a process called triangulation.

When our fellowship began, we soon started having conversations with other fellows and researchers at IAS. Many of these researchers examine mental health from a complexity perspective , in which there is no specific focus on single causative associations between cause and outcome, but where complex, multifaceted models or networks are employed. It was extremely interesting to immerse ourselves in the complexity field, but it also brought up a whole new set of questions, such as when it is appropriate to study individual, causal risk factors in relation to (mental) health, or when this leads to oversimplification. And, important to our research question, how do these complexity approaches relate to other methods, and how can this be integrated in the triangulation framework we were working on?

In an effort to answer these questions, we organized an expert meeting aimed to “gather insights on causality and triangulation from a diverse group of experts, and to improve causal inference for mental health”. We invited experts from a wide range of fields, including genetic epidemiology, complexity/network science, philosophy, and economics, and spent a full day exploring the similarities and differences in our perspectives, and how we may combine these different approaches to improve triangulation.

This expert meeting was one of the key highlights of our fellowship, as it presented us with an opportunity to bring together many different experts with varying opinions who would otherwise be unlikely to meet and engage in discussions. Based on the exciting discussions that took place that day, we are currently working on an opinion piece in which we explore (and contrast) how complexity science and triangulation approaches propose to advance the field of mental health research. In addition to this opinion piece, we hope that these new connections with colleagues across different fields will lead to inspiring future collaborations.

Our time at IAS has truly been transformative, as it has (re-)opened our eyes to the many different perspectives one can take in mental health research. Take as an example the study of major depressive disorder, its causes and its consequences. There are many different perspectives one can take when studying this topic (such as a complexity approach or a (genetic) epidemiological approach), and while these perspectives are not necessarily mutually exclusive, we are strongly guided by the beliefs of those fields and examine results in light of our own theoretical framework.

While we set out to compare how one can combine different types of methods for causal inference, our fellowship at IAS has brought us to think more about how shifting perspectives from one discipline/theoretical framework to another impacts our research. This is certainly not a new idea in science, but our tendency to work within one discipline means we slowly become less and less acquainted with other viewpoints. We would like to thank the IAS for providing us with the opportunity to work in such a multidisciplinary environment, and hope to be part of this network for a much longer time!

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Health Status and Mental Health of Transgender and Gender-Diverse Adults

• 1 Harvard Medical School, Boston, Massachusetts
• 2 Department of Surgery, Brigham and Women’s Hospital, Boston, Massachusetts
• 3 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
• 4 The Fenway Institute, Fenway Health, Boston, Massachusetts
• 5 Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor
• 6 Department of Psychiatry, Massachusetts General Hospital, Boston
• Invited Commentary Association of Political Assaults With the Health of Transgender and Nonbinary Persons Carl G. Streed Jr, MD, MPH; Kellan E. Baker, PhD, MPH, MA; Arjee Javellana Restar, PhD, MPH JAMA Internal Medicine

The National Institutes of Health has designated transgender and gender-diverse (TGD) people as a population that experiences health disparities. A 2017 US study documented physical and mental health inequities between TGD and cisgender adults. 1 Since then, a record number of enacted laws has threatened the rights and protections of TGD people, including restricting access to gender-affirming care and permitting discrimination in public accommodations. 2 , 3 Little is known about how the health of TGD people has changed during this surge in legislation. This study evaluated recent trends in health status and mental health among TGD adults in the US.

• Invited Commentary Association of Political Assaults With the Health of Transgender and Nonbinary Persons JAMA Internal Medicine

Read More About

Liu M , Patel VR , Reisner SL , Keuroghlian AS. Health Status and Mental Health of Transgender and Gender-Diverse Adults. JAMA Intern Med. Published online June 24, 2024. doi:10.1001/jamainternmed.2024.2544

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Home > CHESS > Graduate Teacher Education > 105

Graduate Teacher Education

Impact of choice-based art education on student engagement and mental health.

Carly Christensen Follow

Date of Award

Document type, degree name.

Master of Arts in Education

Differentiated Instruction

Capstone Instructor

Dr. Brian Boothe

Second Reader

Dr. Tosca Grimm

choice-based art education, discipline-based art education, engagement, mental health

This paper reviewed ways art educators effectively incorporated choice-based methods to increase student engagement and support students' mental health. It was essential to study engagement within art class because past models of art education needed more room for authentic student creativity and problem-solving. Additionally, a large population of students struggle with mental health issues, and art naturally supports mental health. This research reviewed fifteen studies, ranging from quantitative, qualitative, and mixed methods. While examining the research, three main themes arose. First, choice-based art education had multiple benefits for student engagement. The next theme illustrated the positive impact of choice on student mental health. The final theme found in this research demonstrated many effective choice-based art education teaching strategies. This analysis discovered the importance of the artistic process, how choice-based art positively affected student behavior, and how choice-based art fostered a sense of community in the art room. These findings invite policymakers and school leadership to support choice-based art education through adequate supplies and teaching spaces, relevant professional development, and smaller class sizes.

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Your gut microbes may influence how you handle stress.

An illustration of the human microbiome. The bacteria in our gut may influence our mental health, research finds. MEHAU KULYK/Getty Images/Science Photo Library RF hide caption

The gut microbiome — the ecosystem of tiny organisms inside us all — has emerged as fertile new territory for studying a range of psychiatric conditions and neurological diseases .

Research has demonstrated the brain and gut are in constant communication and that changes in the microbiome are linked to mood and mental health. Now a study published this month in Nature Mental Health finds distinct biological signatures in the microbiomes of people who are highly resilient in the face of stressful events.

“The accuracy with which these patterns emerged was really amazing,” says Arpana Church , a neuroscientist at UCLA’s Goodman-Luskin Microbiome Center who led the new study.

The research is a jumping off point for future human studies that some researchers believe could ultimately lead to treatments. It may also point the way to biomarkers in the microbiome that can help tailor decisions on how to use existing therapies in mental health.

Studying the link between the gut and mental health is personal for this scientist

Resilience linked to anti-inflammatory microbes.

For their analysis, Church and her team separated 116 adults without a mental health diagnosis into two groups based on how they scored on a scale of psychological resilience.

Next, they sifted through a huge amount of data gathered from brain imaging, stool samples and psychological questionnaires and fed that into a machine-learning model to find patterns.

This analysis of gene activity, metabolites and other information came up with several key associations in the high resilience group. In the brain, there were increased features related to improved emotion regulation and cognition.

“Think about the cognitive part, or the frontal part, of your brain being like the brakes,” says Church. “The highly resilient individuals had really efficient brakes, and less of this hyper-stressed response. ” 

Then they delved into the microbiome, looking not only at the abundance of different microorganisms, but also at their genetic activity to see what they were actually doing.

Two major patterns emerged in people who were more resilient to stress: The activity in their microbiome was linked to reduced inflammation and to improved gut barrier integrity.

This tracks with previous research that has shown patients with a variety of psychiatric conditions have a balance of gut bacteria that includes more of certain pro-inflammatory bacteria and less of those with anti-inflammatory effects.

Patients say keto helps with their mental illness. Science is racing to understand why

Church notes the gut barrier absorbs nutrients and keeps toxins and pathogens from entering the bloodstream. When that becomes more permeable, or “leaky,” the resulting inflammation acts as a stress signal to the brain that all is not well.

Microbes that ‘talk’ to our nervous system

The new study fits into a quickly-expanding body of work on the brain-gut connection.

“I was really excited to see this being done in quite a big human cohort,” says Thomaz Bastiaanssen , a bioinformatician who studies the gut microbiome and mental health at Amsterdam University Medical Center.

In recent years, he says scientists have established that there’s a strong “bi-directional relationship” between the gut and the brain. Much of that is based on preclinical lab studies using animal models, as well as some human observational studies and in vitro work.

“All of this points towards roughly four ways that the microbiome communicates with the host,” says Bastiaanssen.

Along with the immune system, there’s the vagus nerve that functions like a superhighway, running from the brain to the gut and directly interfacing with the microbiome.

These gut microbiota also talk with the central nervous system by secreting neurotransmitters, like serotonin and dopamine (about 90% of serotonin is produced in the gut and about 50% of dopamine).

In addition, the microbiome can produce short-chain fatty acids that help maintain the gut barrier and exert an anti-inflammatory effect on the brain, among other things.

Just last year, Jane Foster , a neuroscientist at UT Southwestern Medical Center, found that a community of bacteria related to the production of these short-chain fatty acids was reduced in people with depression who had elevated anxiety.

In recent years, other observational studies have strengthened the evidence linking gut microbiome and mental health in humans, although there are still many unanswered questions because this research is finding correlations.

For example, large studies from scientists in the Netherlands and elsewhere have found microbiomes with less diversity of bacteria can be predictive of depression, and that having more or less of certain bacteria linked to the synthesis of neurotransmitters and short chain fatty acids may be key .

Foster praised the UCLA study as “novel” because it took a full-body view of the brain-gut-microbiome and its potential role in resilience.

She notes the analysis turned up a link between anxiety and the microbiome, which is already a well-established area of research . More than a decade ago, Foster and others showed this link in lab experiments with “germ-free” mice and anxiety.

In the context of stress, scientists have found even short term exposure to stress can lead to alterations in the microbiome, and that changing the composition of the microbiome could make some mice more resilient to stress.

Probiotic treatments for stress? Not yet

There are growing efforts to move this research into actionable treatments, using diets, prebiotic and probiotic supplements. But Bastiaanssen says the complexity of the microbiome calls for a different approach than what’s typically used in pharmaceutical development, which tends to focus on finding a single molecule or drug.

He says that would be like trying to grow a forest in a desert by planting a few seeds.

“Obviously it’s not going to work,” he says, “because there is no supporting ecosystem.”

He says the microbiome field is still coming out of its infancy stage.

“We've established a link in the microbiome, gut-brain axis. We’ve got really robust evidence,” he says. “The next question we need to understand is, how exactly it works?”

He notes there is some promising evidence from small human studies that have shown targeting the microbiome with certain diets ( one rich in fermented foods ) can reduce inflammation.

Another trial, this one from Bastiaanssen and a team at the University College Cork, found that a diet focused on vegetables and foods known to influence the microbiota, could reduce perceived stress .

While these efforts are completely “valid,” Foster argues the power of these studies is they can lead to the discovery of biomarkers that can help steer decisions about how to use existing treatments and who will be the best candidate.

“ Can I measure something in your microbiome, maybe in your blood and maybe in your brain to determine if you're depressed, should I give you an antidepressant? ... or neurostimulation? Shall I do cognitive behavior therapy or tell you to exercise?”

That could be the value of a holistic marker that can be measured in your microbiome, she says. And she thinks it could become an effective tool for clinical care within the next decade.

For her part, Church envisions, hypothetically, one day leveraging this field of research to “engineer a probiotic blend that could help mitigate stress” and prevent the onset of some diseases.

“The biggest problem is that we need more studies that are actually going to test these in human trials,” she says. She acknowledges there are all sorts of unsubstantiated claims out there when it comes to improving the microbiome. For now she tells people the data isn’t strong enough yet to know which treatment to try.

“There isn't really one out there that's been really tested,” she says, “I say come back to me in a year or more and I'll let you know.”

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Neuroprotective and mental health benefits of salt-tolerant plants: a comprehensive review of traditional uses and biological properties.

1. Introduction

1.1. biochemical targets in mental health disorders, 1.2. role of natural remedies in promoting mental wellness, 1.3. importance of salt-tolerant plants, 2. methodology, 3. traditional uses of salt-tolerant plants as neuroprotective and mental health commodities, 4. salt-tolerant plants as sources of neuroprotective and mental health commodities, 4.1. in vitro assays, 4.2. in vivo studies, 5. conclusions and future research directions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Rodrigues, M.J.; Pereira, C.G.; Custódio, L. Neuroprotective and Mental Health Benefits of Salt-Tolerant Plants: A Comprehensive Review of Traditional Uses and Biological Properties. Appl. Sci. 2024 , 14 , 5534. https://doi.org/10.3390/app14135534

Rodrigues MJ, Pereira CG, Custódio L. Neuroprotective and Mental Health Benefits of Salt-Tolerant Plants: A Comprehensive Review of Traditional Uses and Biological Properties. Applied Sciences . 2024; 14(13):5534. https://doi.org/10.3390/app14135534

Rodrigues, Maria João, Catarina Guerreiro Pereira, and Luísa Custódio. 2024. "Neuroprotective and Mental Health Benefits of Salt-Tolerant Plants: A Comprehensive Review of Traditional Uses and Biological Properties" Applied Sciences 14, no. 13: 5534. https://doi.org/10.3390/app14135534

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Mental Health Prevention and Promotion—A Narrative Review

Associated data.

Extant literature has established the effectiveness of various mental health promotion and prevention strategies, including novel interventions. However, comprehensive literature encompassing all these aspects and challenges and opportunities in implementing such interventions in different settings is still lacking. Therefore, in the current review, we aimed to synthesize existing literature on various mental health promotion and prevention interventions and their effectiveness. Additionally, we intend to highlight various novel approaches to mental health care and their implications across different resource settings and provide future directions. The review highlights the (1) concept of preventive psychiatry, including various mental health promotions and prevention approaches, (2) current level of evidence of various mental health preventive interventions, including the novel interventions, and (3) challenges and opportunities in implementing concepts of preventive psychiatry and related interventions across the settings. Although preventive psychiatry is a well-known concept, it is a poorly utilized public health strategy to address the population's mental health needs. It has wide-ranging implications for the wellbeing of society and individuals, including those suffering from chronic medical problems. The researchers and policymakers are increasingly realizing the potential of preventive psychiatry; however, its implementation is poor in low-resource settings. Utilizing novel interventions, such as mobile-and-internet-based interventions and blended and stepped-care models of care can address the vast mental health need of the population. Additionally, it provides mental health services in a less-stigmatizing and easily accessible, and flexible manner. Furthermore, employing decision support systems/algorithms for patient management and personalized care and utilizing the digital platform for the non-specialists' training in mental health care are valuable additions to the existing mental health support system. However, more research concerning this is required worldwide, especially in the low-and-middle-income countries.

Introduction

Mental disorder has been recognized as a significant public health concern and one of the leading causes of disability worldwide, particularly with the loss of productive years of the sufferer's life ( 1 ). The Global Burden of Disease report (2019) highlights an increase, from around 80 million to over 125 million, in the worldwide number of Disability-Adjusted Life Years (DALYs) attributable to mental disorders. With this surge, mental disorders have moved into the top 10 significant causes of DALYs worldwide over the last three decades ( 2 ). Furthermore, this data does not include substance use disorders (SUDs), which, if included, would increase the estimated burden manifolds. Moreover, if the caregiver-related burden is accounted for, this figure would be much higher. Individual, social, cultural, political, and economic issues are critical mental wellbeing determinants. An increasing burden of mental diseases can, in turn, contribute to deterioration in physical health and poorer social and economic growth of a country ( 3 ). Mental health expenditure is roughly 3–4% of their Gross Domestic Products (GDPs) in developed regions of the world; however, the figure is abysmally low in low-and-middle-income countries (LMICs) ( 4 ). Untreated mental health and behavioral problems in childhood and adolescents, in particular, have profound long-term social and economic adverse consequences, including increased contact with the criminal justice system, lower employment rate and lesser wages among those employed, and interpersonal difficulties ( 5 – 8 ).

Need for Mental Health (MH) Prevention

Longitudinal studies suggest that individuals with a lower level of positive wellbeing are more likely to acquire mental illness ( 9 ). Conversely, factors that promote positive wellbeing and resilience among individuals are critical in preventing mental illnesses and better outcomes among those with mental illness ( 10 , 11 ). For example, in patients with depressive disorders, higher premorbid resilience is associated with earlier responses ( 12 ). On the contrary, patients with bipolar affective- and recurrent depressive disorders who have a lower premorbid quality of life are at higher risk of relapses ( 13 ).

Recently there has been an increased emphasis on the need to promote wellbeing and positive mental health in preventing the development of mental disorders, for poor mental health has significant social and economic implications ( 14 – 16 ). Research also suggests that mental health promotion and preventative measures are cost-effective in preventing or reducing mental illness-related morbidity, both at the society and individual level ( 17 ).

Although the World Health Organization (WHO) defines health as “a state of complete physical, mental, and social wellbeing and not merely an absence of disease or infirmity,” there has been little effort at the global level or stagnation in implementing effective mental health services ( 18 ). Moreover, when it comes to the research on mental health (vis-a-viz physical health), promotive and preventive mental health aspects have received less attention vis-a-viz physical health. Instead, greater emphasis has been given to the illness aspect, such as research on psychopathology, mental disorders, and treatment ( 19 , 20 ). Often, physicians and psychiatrists are unfamiliar with various concepts, approaches, and interventions directed toward mental health promotion and prevention ( 11 , 21 ).

Prevention and promotion of mental health are essential, notably in reducing the growing magnitude of mental illnesses. However, while health promotion and disease prevention are universally regarded concepts in public health, their strategic application for mental health promotion and prevention are often elusive. Furthermore, given the evidence of substantial links between psychological and physical health, the non-incorporation of preventive mental health services is deplorable and has serious ramifications. Therefore, policymakers and health practitioners must be sensitized about linkages between mental- and physical health to effectively implement various mental health promotive and preventive interventions, including in individuals with chronic physical illnesses ( 18 ).

The magnitude of the mental health problems can be gauged by the fact that about 10–20% of young individuals worldwide experience depression ( 22 ). As described above, poor mental health during childhood is associated with adverse health (e.g., substance use and abuse), social (e.g., delinquency), academic (e.g., school failure), and economic (high risk of poverty) adverse outcomes in adulthood ( 23 ). Childhood and adolescence are critical periods for setting the ground for physical growth and mental wellbeing ( 22 ). Therefore, interventions promoting positive psychology empower youth with the life skills and opportunities to reach their full potential and cope with life's challenges. Comprehensive mental health interventions involving families, schools, and communities have resulted in positive physical and psychological health outcomes. However, the data is limited to high-income countries (HICs) ( 24 – 28 ).

In contrast, in low and middle-income countries (LMICs) that bear the greatest brunt of mental health problems, including massive, coupled with a high treatment gap, such interventions remained neglected in public health ( 29 , 30 ). This issue warrants prompt attention, particularly when global development strategies such as Millennium Development Goals (MDGs) realize the importance of mental health ( 31 ). Furthermore, studies have consistently reported that people with socioeconomic disadvantages are at a higher risk of mental illness and associated adverse outcomes; partly, it is attributed to the inequitable distribution of mental health services ( 32 – 35 ).

Scope of Mental Health Promotion and Prevention in the Current Situation

Literature provides considerable evidence on the effectiveness of various preventive mental health interventions targeting risk and protective factors for various mental illnesses ( 18 , 36 – 42 ). There is also modest evidence of the effectiveness of programs focusing on early identification and intervention for severe mental diseases (e.g., schizophrenia and psychotic illness, and bipolar affective disorders) as well as common mental disorders (e.g., anxiety, depression, stress-related disorders) ( 43 – 46 ). These preventive measures have also been evaluated for their cost-effectiveness with promising findings. In addition, novel interventions such as digital-based interventions and novel therapies (e.g., adventure therapy, community pharmacy program, and Home-based Nurse family partnership program) to address the mental health problems have yielded positive results. Likewise, data is emerging from LMICs, showing at least moderate evidence of mental health promotion intervention effectiveness. However, most of the available literature and intervention is restricted mainly to the HICs ( 47 ). Therefore, their replicability in LMICs needs to be established and, also, there is a need to develop locally suited interventions.

Fortunately, there has been considerable progress in preventive psychiatry over recent decades, including research on it. In the light of these advances, there is an accelerated interest among researchers, clinicians, governments, and policymakers to harness the potentialities of the preventive strategies to improve the availability, accessibility, and utility of such services for the community.

The Concept of Preventive Psychiatry

Origins of preventive psychiatry.

The history of preventive psychiatry can be traced back to the early 1900's with the foundation of the national mental health association (erstwhile mental health association), the committee on mental hygiene in New York, and the mental health hygiene movement ( 48 ). The latter emphasized the need for physicians to develop empathy and recognize and treat mental illness early, leading to greater awareness about mental health prevention ( 49 ). Despite that, preventive psychiatry remained an alien concept for many, including mental health professionals, particularly when the etiology of most psychiatric disorders was either unknown or poorly understood. However, recent advances in our understanding of the phenomena underlying psychiatric disorders and availability of the neuroimaging and electrophysiological techniques concerning mental illness and its prognosis has again brought the preventive psychiatry in the forefront ( 1 ).

Levels of Prevention

The literal meaning of “prevention” is “the act of preventing something from happening” ( 50 ); the entity being prevented can range from the risk factors of the development of the illness, the onset of illness, or the recurrence of the illness or associated disability. The concept of prevention emerged primarily from infectious diseases; measures like mass vaccination and sanitation promotion have helped prevent the development of the diseases and subsequent fatalities. The original preventive model proposed by the Commission on Chronic Illness in 1957 included primary, secondary, and tertiary preventions ( 48 ).

The Concept of Primary, Secondary, and Tertiary Prevention

The stages of prevention target distinct aspects of the illness's natural course; the primary prevention acts at the stage of pre-pathogenesis, that is, when the disease is yet to occur, whereas the secondary and tertiary prevention target the phase after the onset of the disease ( 51 ). Primary prevention includes health promotion and specific protection, while secondary and tertairy preventions include early diagnosis and treatment and measures to decrease disability and rehabilitation, respectively ( 51 ) ( Figure 1 ).

The concept of primary and secondary prevention [adopted from prevention: Primary, Secondary, Tertiary by Bauman et al. ( 51 )].

The primary prevention targets those individuals vulnerable to developing mental disorders and their consequences because of their bio-psycho-social attributes. Therefore, it can be viewed as an intervention to prevent an illness, thereby preventing mental health morbidity and potential social and economic adversities. The preventive strategies under it usually target the general population or individuals at risk. Secondary and tertiary prevention targets those who have already developed the illness, aiming to reduce impairment and morbidity as soon as possible. However, these measures usually occur in a person who has already developed an illness, therefore facing related suffering, hence may not always be successful in curing or managing the illness. Thus, secondary and tertiary prevention measures target the already exposed or diagnosed individuals.

The Concept of Universal, Selective, and Indicated Prevention

The classification of health prevention based on primary/secondary/tertiary prevention is limited in being highly centered on the etiology of the illness; it does not consider the interaction between underlying etiology and risk factors of an illness. Gordon proposed another model of prevention that focuses on the degree of risk an individual is at, and accordingly, the intensity of intervention is determined. He has classified it into universal, selective, and indicated prevention. A universal preventive strategy targets the whole population irrespective of individual risk (e.g., maintaining healthy, psychoactive substance-free lifestyles); selective prevention is targeted to those at a higher risk than the general population (socio-economically disadvantaged population, e.g., migrants, a victim of a disaster, destitute, etc.). The indicated prevention aims at those who have established risk factors and are at a high risk of getting the disease (e.g., family history of psychiatric illness, history of substance use, certain personality types, etc.). Nevertheless, on the other hand, these two classifications (the primary, secondary, and tertiary prevention; and universal, selective, and indicated prevention) have been intended for and are more appropriate for physical illnesses with a clear etiology or risk factors ( 48 ).

In 1994, the Institute of Medicine (IOM) Committee on Prevention of Mental Disorders proposed a new paradigm that classified primary preventive measures for mental illnesses into three categories. These are indicated, selected, and universal preventive interventions (refer Figure 2 ). According to this paradigm, primary prevention was limited to interventions done before the onset of the mental illness ( 48 ). In contrast, secondary and tertiary prevention encompasses treatment and maintenance measures ( Figure 2 ).

The interventions for mental illness as classified by the Institute of Medicine (IOM) Committee on Prevention of Mental Disorders [adopted from Mrazek and Haggerty ( 48 )].

Although the boundaries between prevention and treatment are often more overlapping than being exclusive, the new paradigm can be used to avoid confusion stemming from the common belief that prevention can take place at all parts of mental health management ( 48 ). The onset of mental illnesses can be prevented by risk reduction interventions, which can involve reducing risk factors in an individual and strengthening protective elements in them. It aims to target modifiable factors, both risk, and protective factors, associated with the development of the illness through various general and specific interventions. These interventions can work across the lifespan. The benefits are not restricted to reduction or delay in the onset of illness but also in terms of severity or duration of illness ( 48 ).On the spectrum of mental health interventions, universal preventive interventions are directed at the whole population without identifiable risk factors. The interventions are beneficial for the general population or sub-groups. Prenatal care and childhood vaccination are examples of preventative measures that have benefited both physical and mental health. Selective preventive mental health interventions are directed at people or a subgroup with a significantly higher risk of developing mental disorders than the general population. Risk groups are those who, because of their vulnerabilities, are at higher risk of developing mental illnesses, e.g., infants with low-birth-weight (LBW), vulnerable children with learning difficulties or victims of maltreatment, elderlies, etc. Specific interventions are home visits and new-born day care facilities for LBW infants, preschool programs for all children living in resource-deprived areas, support groups for vulnerable elderlies, etc. Indicated preventive interventions focus on high-risk individuals who have developed minor but observable signs or symptoms of mental disorder or genetic risk factors for mental illness. However, they have not fulfilled the criteria of a diagnosable mental disorder. For instance, the parent-child interaction training program is an indicated prevention strategy that offers support to children whose parents have recognized them as having behavioral difficulties.

The overall objective of mental health promotion and prevention is to reduce the incidence of new cases, additionally delaying the emergence of mental illness. However, promotion and prevention in mental health complement each other rather than being mutually exclusive. Moreover, combining these two within the overall public health framework reduces stigma, increases cost-effectiveness, and provides multiple positive outcomes ( 18 ).

How Prevention in Psychiatry Differs From Other Medical Disorders

Compared to physical illnesses, diagnosing a mental illness is more challenging, particularly when there is still a lack of objective assessment methods, including diagnostic tools and biomarkers. Therefore, the diagnosis of mental disorders is heavily influenced by the assessors' theoretical perspectives and subjectivity. Moreover, mental illnesses can still be considered despite an individual not fulfilling the proper diagnostic criteria led down in classificatory systems, but there is detectable dysfunction. Furthermore, the precise timing of disorder initiation or transition from subclinical to clinical condition is often uncertain and inconclusive ( 48 ). Therefore, prevention strategies are well-delineated and clear in the case of physical disorders while it's still less prevalent in mental health parlance.

Terms, Definitions, and Concepts

The terms mental health, health promotion, and prevention have been differently defined and interpreted. It is further complicated by overlapping boundaries of the concept of promotion and prevention. Some commonly used terms in mental health prevention have been tabulated ( Table 1 ) ( 18 ).

Commonly used terms in mental health prevention.

Mental Health Promotion and Protection

The term “mental health promotion” also has definitional challenges as it signifies different things to different individuals. For some, it means the treatment of mental illness; for others, it means preventing the occurrence of mental illness; while for others, it means increasing the ability to manage frustration, stress, and difficulties by strengthening one's resilience and coping abilities ( 54 ). It involves promoting the value of mental health and improving the coping capacities of individuals rather than amelioration of symptoms and deficits.

Mental health promotion is a broad concept that encompasses the entire population, and it advocates for a strengths-based approach and tries to address the broader determinants of mental health. The objective is to eliminate health inequalities via empowerment, collaboration, and participation. There is mounting evidence that mental health promotion interventions improve mental health, lower the risk of developing mental disorders ( 48 , 55 , 56 ) and have socioeconomic benefits ( 24 ). In addition, it strives to increase an individual's capacity for psychosocial wellbeing and adversity adaptation ( 11 ).

However, the concepts of mental health promotion, protection, and prevention are intrinsically linked and intertwined. Furthermore, most mental diseases result from complex interaction risk and protective factors instead of a definite etiology. Facilitating the development and timely attainment of developmental milestones across an individual's lifespan is critical for positive mental health ( 57 ). Although mental health promotion and prevention are essential aspects of public health with wide-ranging benefits, their feasibility and implementation are marred by financial and resource constraints. The lack of cost-effectiveness studies, particularly from the LMICs, further restricts its full realization ( 47 , 58 , 59 ).

Despite the significance of the topic and a considerable amount of literature on it, a comprehensive review is still lacking that would cover the concept of mental health promotion and prevention and simultaneously discusses various interventions, including the novel techniques delivered across the lifespan, in different settings, and level of prevention. Therefore, this review aims to analyze the existing literature on various mental health promotion and prevention-based interventions and their effectiveness. Furthermore, its attempts to highlight the implications of such intervention in low-resource settings and provides future directions. Such literature would add to the existing literature on mental health promotion and prevention research and provide key insights into the effectiveness of such interventions and their feasibility and replicability in various settings.

Methodology

For the current review, key terms like “mental health promotion,” OR “protection,” OR “prevention,” OR “mitigation” were used to search relevant literature on Google Scholar, PubMed, and Cochrane library databases, considering a time period between 2000 to 2019 ( Supplementary Material 1 ). However, we have restricted our search till 2019 for non-original articles (reviews, commentaries, viewpoints, etc.), assuming that it would also cover most of the original articles published until then. Additionally, we included original papers from the last 5 years (2016–2021) so that they do not get missed out if not covered under any published review. The time restriction of 2019 for non-original articles was applied to exclude papers published during the Coronavirus disease (COVID-19) pandemic as the latter was a significant event, bringing about substantial change and hence, it warranted a different approach to cater to the MH needs of the population, including MH prevention measures. Moreover, the COVID-19 pandemic resulted in the flooding of novel interventions for mental health prevention and promotion, specifically targeting the pandemic and its consequences, which, if included, could have biased the findings of the current review on various MH promotion and prevention interventions.

A time frame of about 20 years was taken to see the effectiveness of various MH promotion and protection interventions as it would take substantial time to be appreciated in real-world situations. Therefore, the current paper has put greater reliance on the review articles published during the last two decades, assuming that it would cover most of the original articles published until then.

The above search yielded 320 records: 225 articles from Google scholar, 59 articles from PubMed, and 36 articles from the Cochrane database flow-diagram of records screening. All the records were title/abstract screened by all the authors to establish the suitability of those records for the current review; a bibliographic- and gray literature search was also performed. In case of any doubts or differences in opinion, it was resolved by mutual discussion. Only those articles directly related to mental health promotion, primary prevention, and related interventions were included in the current review. In contrast, records that discussed any specific conditions/disorders (post-traumatic stress disorders, suicide, depression, etc.), specific intervention (e.g., specific suicide prevention intervention) that too for a particular population (e.g., disaster victims) lack generalizability in terms of mental health promotion or prevention, those not available in the English language, and whose full text was unavailable were excluded. The findings of the review were described narratively.

Interventions for Mental Health Promotion and Prevention and Their Evidence

Various interventions have been designed for mental health promotion and prevention. They are delivered and evaluated across the regions (high-income countries to low-resource settings, including disaster-affiliated regions of the world), settings (community-based, school-based, family-based, or individualized); utilized different psychological constructs and therapies (cognitive behavioral therapy, behavioral interventions, coping skills training, interpersonal therapies, general health education, etc.); and delivered by different professionals/facilitators (school-teachers, mental health professionals or paraprofessionals, peers, etc.). The details of the studies, interventions used, and outcomes have been provided in Supplementary Table 1 . Below we provide the synthesized findings of the available research.

The majority of the available studies were quantitative and experimental. Randomized controlled trials comprised a sizeable proportion of the studies; others were quasi-experimental studies and, a few, qualitative studies. The studies primarily focussed on school students or the younger population, while others were explicitly concerned with the mental health of young females ( 60 ). Newer data is emerging on mental health promotion and prevention interventions for elderlies (e.g., dementia) ( 61 ). The majority of the research had taken a broad approach to mental health promotion ( 62 ). However, some studies have focused on universal prevention ( 63 , 64 ) or selective prevention ( 65 – 68 ). For instance, the Resourceful Adolescent Program (RAPA) was implemented across the schools and has utilized cognitive-behavioral and interpersonal therapies and reported a significant improvement in depressive symptoms. Some of the interventions were directed at enhancing an individual's characteristics like resilience, behavior regulation, and coping skills (ZIPPY's Friends) ( 69 ), while others have focused on the promotion of social and emotional competencies among the school children and attempted to reduce the gap in such competencies across the socio-economic classes (“Up” program) ( 70 ) or utilized expressive abilities of the war-affected children (Writing for Recover (WfR) intervention) ( 71 ) to bring about an improvement in their psychological problems (a type of selective prevention) ( 62 ) or harnessing the potential of Art, in the community-based intervention, to improve self-efficacy, thus preventing mental disorders (MAD about Art program) ( 72 ). Yet, others have focused on strengthening family ( 60 , 73 ), community relationships ( 62 ), and targeting modifiable risk factors across the life course to prevent dementia among the elderlies and also to support the carers of such patients ( 61 ).

Furthermore, more of the studies were conducted and evaluated in the developed parts of the world, while emerging economies, as anticipated, far lagged in such interventions or related research. The interventions that are specifically adapted for local resources, such as school-based programs involving paraprofessionals and teachers in the delivery of mental health interventions, were shown to be more effective ( 62 , 74 ). Likewise, tailored approaches for low-resource settings such as LMICs may also be more effective ( 63 ). Some of these studies also highlight the beneficial role of a multi-dimensional approach ( 68 , 75 ) and interventions targeting early lifespan ( 76 , 77 ).

Newer Insights: How to Harness Digital Technology and Novel Methods of MH Promotion and Protection

With the advent of digital technology and simultaneous traction on mental health promotion and prevention interventions, preventive psychiatrists and public health experts have developed novel techniques to deliver mental health promotive and preventive interventions. These encompass different settings (e.g., school, home, workplace, the community at large, etc.) and levels of prevention (universal, selective, indicated) ( 78 – 80 ).

The advanced technologies and novel interventions have broadened the scope of MH promotion and prevention, such as addressing the mental health issues of individuals with chronic medical illness ( 81 , 82 ), severe mental disorders ( 83 ), children and adolescents with mental health problems, and geriatric population ( 78 ). Further, it has increased the accessibility and acceptability of such interventions in a non-stigmatizing and tailored manner. Moreover, they can be integrated into the routine life of the individuals.

For instance, Internet-and Mobile-based interventions (IMIs) have been utilized to monitor health behavior as a form of MH prevention and a stand-alone self-help intervention. Moreover, the blended approach has expanded the scope of MH promotive and preventive interventions such as face-to-face interventions coupled with remote therapies. Simultaneously, it has given way to the stepped-care (step down or step-up care) approach of treatment and its continuation ( 79 ). Also, being more interactive and engaging is particularly useful for the youth.

The blended model of care has utilized IMIs to a varying degree and at various stages of the psychological interventions. This includes IMIs as a supplementary approach to the face-to-face-interventions (FTFI), FTFI augmented by behavior intervention technologies (BITs), BITs augmented by remote human support, and fully automated BITs ( 84 ).

The stepped care model of mental health promotion and prevention strategies includes a stepped-up approach, wherein BITs are utilized to manage the prodromal symptoms, thereby preventing the onset of the full-blown episode. In the Stepped-down approach, the more intensive treatments (in-patient or out-patient based interventions) are followed and supplemented with the BITs to prevent relapse of the mental illness, such as for previously admitted patients with depression or substance use disorders ( 85 , 86 ).

Similarly, the latest research has developed newer interventions for strengthening the psychological resilience of the public or at-risk individuals, which can be delivered at the level of the home, such as, e.g., nurse family partnership program (to provide support to the young and vulnerable mothers and prevent childhood maltreatment) ( 87 ); family healing together program aimed at improving the mental health of the family members living with persons with mental illness (PwMI) ( 88 ). In addition, various novel interventions for MH promotion and prevention have been highlighted in the Table 2 .

Depiction of various novel mental health promotion and prevention strategies.

a/w, associated with; A-V, audio-visual; b/w, between; CBT, Cognitive Behavioral Therapy; CES-Dep., Center for Epidemiologic Studies-Depression scale; CG, control group; FU, follow-up; GAD, generalized anxiety disorders-7; IA, intervention arm; HCWs, Health Care Workers; LMIC, low and middle-income countries; MDD, major depressive disorders; mgt, management; MH, mental health; MHP, mental health professional; MINI, mini neuropsychiatric interview; NNT, number needed to treat; PHQ-9, patient health questionnaire; TAU, treatment as usual .

Furthermore, school/educational institutes-based interventions such as school-Mental Health Magazines to increase mental health literacy among the teachers and students have been developed ( 80 ). In addition, workplace mental health promotional activities have targeted the administrators, e.g., guided “e-learning” for the managers that have shown to decrease the mental health problems of the employees ( 102 ).

Likewise, digital technologies have also been harnessed in strengthening community mental health promotive/preventive services, such as the mental health first aid (MHFA) Books on Prescription initiative in New Zealand provided information and self-help tools through library networks and trained book “prescribers,” particularly in rural and remote areas ( 103 ).

Apart from the common mental disorders such as depression, anxiety, and behavioral disorders in the childhood/adolescents, novel interventions have been utilized to prevent the development of or management of medical, including preventing premature mortality and psychological issues among the individuals with severe mental illnesses (SMIs), e.g., Lets' talk about tobacco-web based intervention and motivational interviewing to prevent tobacco use, weight reduction measures, and promotion of healthy lifestyles (exercise, sleep, and balanced diets) through individualized devices, thereby reducing the risk of cardiovascular disorders ( 83 ). Similarly, efforts have been made to improve such individuals' coping skills and employment chances through the WorkingWell mobile application in the US ( 104 ).

Apart from the digital-based interventions, newer, non-digital-based interventions have also been utilized to promote mental health and prevent mental disorders among individuals with chronic medical conditions. One such approach in adventure therapy aims to support and strengthen the multi-dimensional aspects of self. It includes the physical, emotional or cognitive, social, spiritual, psychological, or developmental rehabilitation of the children and adolescents with cancer. Moreover, it is delivered in the natural environment outside the hospital premises, shifting the focus from the illness model to the wellness model ( 81 ). Another strength of this intervention is it can be delivered by the nurses and facilitate peer support and teamwork.

Another novel approach to MH prevention is gut-microbiota and dietary interventions. Such interventions have been explored with promising results for the early developmental disorders (Attention deficit hyperactive disorder, Autism spectrum disorders, etc.) ( 105 ). It works under the framework of the shared vulnerability model for common mental disorders and other non-communicable diseases and harnesses the neuroplasticity potential of the developing brain. Dietary and lifestyle modifications have been recommended for major depressive disorders by the Clinical Practice Guidelines in Australia ( 106 ). As most childhood mental and physical disorders are determined at the level of the in-utero and early after the birth period, targeting maternal nutrition is another vital strategy. The utility has been expanded from maternal nutrition to women of childbearing age. The various novel mental health promotion and prevention strategies are shown in Table 2 .

Newer research is emerging that has utilized the digital platform for training non-specialists in diagnosis and managing individuals with mental health problems, such as Atmiyata Intervention and The SMART MH Project in India, and The Allillanchu Project in Peru, to name a few ( 99 ). Such frameworks facilitate task-sharing by the non-specialist and help in reducing the treatment gap in these countries. Likewise, digital algorithms or decision support systems have been developed to make mental health services more transparent, personalized, outcome-driven, collaborative, and integrative; one such example is DocuMental, a clinical decision support system (DSS). Similarly, frameworks like i-PROACH, a cloud-based intelligent platform for research outcome assessment and care in mental health, have expanded the scope of the mental health support system, including promoting research in mental health ( 100 ). In addition, COVID-19 pandemic has resulted in wider dissemination of the applications based on the evidence-based psycho-social interventions such as National Health Service's (NHS's) Mind app and Headspace (teaching meditation via a website or a phone application) that have utilized mindfulness-based practices to address the psychological problems of the population ( 101 ).

Challenges in Implementing Novel MH Promotion and Prevention Strategies

Although novel interventions, particularly internet and mobile-based interventions (IMIs), are effective models for MH promotion and prevention, their cost-effectiveness requires further exploration. Moreover, their feasibility and acceptability in LMICs could be challenging. Some of these could be attributed to poor digital literacy, digital/network-related limitations, privacy issues, and society's preparedness to implement these interventions.

These interventions need to be customized and adapted according to local needs and context, for which implementation and evaluative research are warranted. In addition, the infusion of more human and financial resources for such activities is required. Some reports highlight that many of these interventions do not align with the preferences and use the pattern of the service utilizers. For instance, one explorative research on mental health app-based interventions targeting youth found that despite the burgeoning applications, they are not aligned with the youth's media preferences and learning patterns. They are less interactive, have fewer audio-visual displays, are not youth-specific, are less dynamic, and are a single touch app ( 107 ).

Furthermore, such novel interventions usually come with high costs. In low-resource settings where service utilizers have limited finances, their willingness to use such services may be doubtful. Moreover, insurance companies, including those in high-income countries (HICs), may not be willing to fund such novel interventions, which restricts the accessibility and availability of interventions.

Research points to the feasibility and effectiveness of incorporating such novel interventions in routine services such as school, community, primary care, or settings, e.g., in low-resource settings, the resource persons like teachers, community health workers, and primary care physicians are already overburdened. Therefore, their willingness to take up additional tasks may raise skepticism. Moreover, the attitudinal barrier to moving from the traditional service delivery model to the novel methods may also impede.

Considering the low MH budget and less priority on the MH prevention and promotion activities in most low-resource settings, the uptake of such interventions in the public health framework may be lesser despite the latter's proven high cost-effectiveness. In contrast, policymakers may be more inclined to invest in the therapeutic aspects of MH.

Such interventions open avenues for personalized and precision medicine/health care vs. the traditional model of MH promotion and preventive interventions ( 108 , 109 ). For instance, multivariate prediction algorithms with methods of machine learning and incorporating biological research, such as genetics, may help in devising tailored, particularly for selected and indicated prevention, interventions for depression, suicide, relapse prevention, etc. ( 79 ). Therefore, more research in this area is warranted.

To be more clinically relevant, greater biological research in MH prevention is required to identify those at higher risk of developing given mental disorders due to the existing risk factors/prominent stress ( 110 ). For instance, researchers have utilized the transcriptional approach to identify a biological fingerprint for susceptibility (denoting abnormal early stress response) to develop post-traumatic stress disorders among the psychological trauma survivors by analyzing the expression of the Peripheral blood mononuclear cell gene expression profiles ( 111 ). Identifying such biological markers would help target at-risk individuals through tailored and intensive interventions as a form of selected prevention.

Similarly, such novel interventions can help in targeting the underlying risk such as substance use, poor stress management, family history, personality traits, etc. and protective factors, e.g., positive coping techniques, social support, resilience, etc., that influences the given MH outcome ( 79 ). Therefore, again, it opens the scope of tailored interventions rather than a one-size-fits-all model of selective and indicated prevention for various MH conditions.

Furthermore, such interventions can be more accessible for the hard-to-reach populations and those with significant mental health stigma. Finally, they play a huge role in ensuring the continuity of care, particularly when community-based MH services are either limited or not available. For instance, IMIs can maintain the improvement of symptoms among individuals previously managed in-patient, such as for suicide, SUDs, etc., or receive intensive treatment like cognitive behavior therapy (CBT) for depression or anxiety, thereby helping relapse prevention ( 86 , 112 ). Hence, such modules need to be developed and tested in low-resource settings.

IMIs (and other novel interventions) being less stigmatizing and easily accessible, provide a platform to engage individuals with chronic medical problems, e.g., epilepsy, cancer, cardiovascular diseases, etc., and non-mental health professionals, thereby making it more relevant and appealing for them.

Lastly, research on prevention-interventions needs to be more robust to adjust for the pre-intervention matching, high attrition rate, studying the characteristics of treatment completers vs. dropouts, and utilizing the intention-to-treat analysis to gauge the effect of such novel interventions ( 78 ).

Recommendations for Low-and-Middle-Income Countries

Although there is growing research on the effectiveness and utility of mental health promotion/prevention interventions across the lifespan and settings, low-resource settings suffer from specific limitations that restrict the full realization of such public health strategies, including implementing the novel intervention. To overcome these challenges, some of the potential solutions/recommendations are as follows:

• The mental health literacy of the population should be enhanced through information, education, and communication (IEC) activities. In addition, these activities should reduce stigma related to mental problems, early identification, and help-seeking for mental health-related issues.
• Involving teachers, workplace managers, community leaders, non-mental health professionals, and allied health staff in mental health promotion and prevention is crucial.
• Mental health concepts and related promotion and prevention should be incorporated into the education curriculum, particularly at the medical undergraduate level.
• Training non-specialists such as community health workers on mental health-related issues across an individual's life course and intervening would be an effective strategy.
• Collaborating with specialists from other disciplines, including complementary and alternative medicines, would be crucial. A provision of an integrated health system would help in increasing awareness, early identification, and prompt intervention for at-risk individuals.
• Low-resource settings need to develop mental health promotion interventions such as community-and school-based interventions, as these would be more culturally relevant, acceptable, and scalable.
• Utilizing a digital platform for scaling mental health services (e.g., telepsychiatry services to at-risk populations) and training the key individuals in the community would be a cost-effective framework that must be explored.
• Infusion of higher financial and human resources in this area would be a critical step, as, without adequate resources, research, service development, and implementation would be challenging.
• It would also be helpful to identify vulnerable populations and intervene in them to prevent the development of clinical psychiatric disorders.
• Lastly, involving individuals with lived experiences at the level of mental health planning, intervention development, and delivery would be cost-effective.

Clinicians, researchers, public health experts, and policymakers have increasingly realized mental health promotion and prevention. Investment in Preventive psychiatry appears to be essential considering the substantial burden of mental and neurological disorders and the significant treatment gap. Literature suggests that MH promotive and preventive interventions are feasible and effective across the lifespan and settings. Moreover, various novel interventions (e.g., internet-and mobile-based interventions, new therapies) have been developed worldwide and proven effective for mental health promotion and prevention; such interventions are limited mainly to HICs.

Despite the significance of preventive psychiatry in the current world and having a wide-ranging implication for the wellbeing of society and individuals, including those suffering from chronic medical problems, it is a poorly utilized public health field to address the population's mental health needs. Lately, researchers and policymakers have realized the untapped potentialities of preventive psychiatry. However, its implementation in low-resource settings is still in infancy and marred by several challenges. The utilization of novel interventions, such as digital-based interventions, and blended and stepped-care models of care, can address the enormous mental health need of the population. Additionally, it provides mental health services in a less-stigmatizing and easily accessible, and flexible manner. More research concerning this is required from the LMICs.

Author Contributions

VS, AK, and SG: methodology, literature search, manuscript preparation, and manuscript review. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2022.898009/full#supplementary-material

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Understanding the Availability of Mental Telehealth Services

June 26, 2024 • Research Highlight

During the coronavirus pandemic, public health measures and restrictions impacted in-person health care visits, leading to a surge in telehealth services as a way of accessing assessment and treatment. Particularly in mental health care, telehealth saw a significant rise, and usage remains high even post-pandemic. However, despite the increased utilization of telehealth services, there's a limited understanding of the availability and structure of these services.

What did the researchers do?

In an NIMH-funded study, researchers led by Jonathan Cantor, Ph.D.   , of the RAND Corporation investigated the availability of different types of telehealth services and the time it took patients to access telehealth care.

Between December 2022 and March 2023, researchers contacted more than 1,900 outpatient mental health care facilities to ask about telehealth services. The underlying sample came from outpatient mental health treatment facilities, not individual practitioners.

The researchers used a secret shopper approach, using a script that mirrored information a prospective patient might ask when inquiring about telehealth services. The secret shoppers asked about the availability of telehealth services for treating major depressive disorder, generalized anxiety disorder, or schizophrenia. They also asked about the specific services offered via telehealth (behavioral therapy, medication management, diagnostic services) and the number of days they would have to wait before having their first telehealth appointment. Both men and women served as secret shoppers, and the names used by the shoppers were chosen to reflect a variety of racial and ethnic backgrounds.

What did the researchers find?

Out of the more than 1,900 facilities contacted, the researchers received replies from 1,404. Among these, 1,221 were accepting new patients. Of those 1,221 facilities, 80% (980) offered telehealth services. Out of the 980 treatment facilities that offered telehealth services:

• 97% provided counseling services
• 77% provided medication management
• 96% provided diagnostic services

Among the facilities that responded to the telehealth question, the researchers found:

• Not-for-profit and for-profit private treatment facilities were more likely to offer telehealth services than public treatment facilities.
• Treatment facilities in metropolitan areas were more likely than non-urban areas to offer medication management but less likely to offer diagnostic services.
• The average wait time for a telehealth appointment was 14 days (ranging from 4 to 75 days, depending on the facility contacted).

What do the findings mean?

The researchers found that some of the facilities they initially reached out to for information did not respond, suggesting that people looking for any type of mental health care may experience barriers to accessing it.

Of the facilities that did respond, most were accepting new patients, and most provided telehealth services; however, the availability of those services and the type of care offered varied by location and state. This suggests there may be disparities in access to telehealth services across the United States.

The researchers note that telehealth services and availability may differ at health centers not included in this study and that the availability of technology that makes telehealth possible—such as broadband services—was not examined in this analysis.

Cantor, J., Schuler, M. S., Matthews, S., Kofner, A., Breslau, J., & McBain, R. K. (2024). Availability of mental telehealth services in the US. JAMA Health Forum , 5 (2), Article e235142. https://doi.org/10.1001/jamahealthforum.2023.5142  

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College instructors feeling the strain of mental health crisis.

Majority of college instructors say they are struggling with mental health issues, which may be impacting their ability to teach effectively

HOBOKEN, NJ— College instructors are feeling the strain of mental health issues and experiencing increasing levels of burnout and stress from the demands of their jobs, according to a new survey by Wiley (NYSE: WLY), one of the world’s largest publishers and a global leader in research and learning .

The Instructor Mental Health Landscape —the last in a series of three Wiley studies exploring mental health issues across the practitioner and higher education landscape—reports six out of ten college instructors (61%) say they are struggling with their mental health to some degree. And more than a third said teaching was more stressful than during the previous academic year.

Instructors cite a number of factors that are contributing to mental health issues they are experiencing, including:

• ongoing pressure to meet the increasing demands and needs of students (51%),
• being asked to do more with less time and resources (49%),
• spending too much time on non-teaching related tasks (48%), and
• feeling underpaid for the work they do (43%).

The increase in mental health struggles may well be having a negative impact on instructors’ ability to teach effectively. Fifteen percent say they feel burned out or exhausted, while another 15% report having less energy and patience.

Among various challenges instructors face, keeping students engaged and helping them retain class material are high on the list. Sixty-five percent say it’s a challenge to keep students engaged, while 53% say that they struggle with teaching students who start courses with different knowledge levels. These findings are aligned with those of Wiley’s March 2024 student mental health survey , where 61% of students said staying engaged and remaining interested in classes was a challenge.

“The mental health crisis is pervasive, impacting college instructors just as we saw it hurting students and practitioners in our previous surveys,” said Amanda Miller, Wiley group vice president, Academic Publishing Group. “It’s important for college officials to understand and acknowledge the strain felt by students and instructors as a result of these issues and continue to take whatever steps they can to help ease the impact on their education and their lives.”

The findings suggest many instructors believe the COVID-19 pandemic may have contributed to their mental health issues. Fifty-seven percent said the pandemic had a negative impact on them, and 83% feel it had a negative impact on students.

College instructors are well aware that students are also experiencing mental health issues. Nearly all respondents (93%) say they believe students are struggling emotionally either a little or significantly. That aligns with the findings of Wiley’s recent report on students which showed more than 80% of college students saying they are struggling emotionally at least somewhat.

Recommendations

Wiley’s report recommends steps colleges and universities can take to support both instructors and students as they navigate the mental health crisis.

• Increasing support —As instructors feel increasingly exhausted and experience more burnout, they’ll need more support. Support networks for instructors can go a long way in improving mental health for both instructors and students.
• Recognizing burnout —Burnout is an issue that can be addressed through various means, including peer support networks, time-saving resources, and strategies for self-care.
• Providing resources —Instructors need materials that are easy to implement, and which save them time and effort. With student engagement a big challenge for many instructors, materials should be flexible, interesting, and provide more opportunity for class interaction.

Wiley Mental Health Surveys

This report is the third in a series of three surveys focusing on mental health issues impacting the higher education community. The first report, The Psychology Practitioner: Navigating High Demand, Burnout, and Telemedicine , issued in December 2023, focused on mental health practitioners, while the second, The Student Mental Health Landscape , issued in March 2024, focused on college students .

Wiley is a leading publisher in psychology resources. Known for its timely subject matter, quality content, and innovative resources, Wiley’s publications and franchises are trusted by practitioners worldwide. For more resources, visit the Wiley Psychology Hub .

Methodology

The survey was completed in December 2023 through January 2024 by 971 instructors at four-year and two-year public and private colleges of varying sizes across the United States.

The full report can be accessed HERE .

About Wiley  

Wiley (NYSE: WLY) is one of the world’s largest publishers and a trusted leader in research and learning. Our industry-leading content, services, platforms, and knowledge networks are tailored to meet the evolving needs of our customers and partners, including researchers, students, instructors, professionals, institutions, and corporations. We empower knowledge-seekers to transform today’s biggest obstacles into tomorrow’s brightest opportunities. For more than two centuries, Wiley has been delivering on its timeless mission to unlock human potential. Visit us at Wiley.com . Follow us on Facebook , Twitter , LinkedIn and Instagram .   

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