Individuals of refugee background resettled in regional and rural Australia: A systematic review of mental health research

Abstract Community-based participatory research (CBPR), a bottom-up approach that community stakeholders and academics are involved equitably, is an effective approach for enhancing relevance and value in public health research and has gained popularity in recent decades. However, little is known about how CBPR can be used in mental health studies with older adults. This systematic review examined the current state of knowledge about how CBPR approach has been adopted in mental health research among older adults in different societies. According to the PRISMA guidelines, we searched five major databases and screened the literature using these criteria: 1) journal articles reporting use of CBPR in mental health research among older adults, 2) articles published in English language, 3) studies conducted in any settings with any mental health research. Initial search found 3,227 articles and preliminary screening identified 23 eligible articles. We found that around 90% of studies were conducted in the West. Most studies adopted CBPR to develop community-based mental health interventions or to revise current interventions or models while addressing the cultural needs of their studied population. Few studies adopted CBPR to evaluate existing mental health workshops or programmes. The extent of involvement of older adults in the CBPR approach varied across studies, from questionnaire design to programme evaluation. Our review uncovered ways of CBPR implementation across different societies and elements of successful implementation in CBPR practices in mental health research among older adults.

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Similarities and Differences of Mental Health in Women and Men: A Systematic Review of Findings in Three Large German Cohorts

Frontiers in Public Health ◽

10.3389/fpubh.2021.553071 ◽

2021 ◽

Vol 9 ◽

Author(s):

Daniëlle Otten ◽

Ana N. Tibubos ◽

Georg Schomerus ◽

Elmar Brähler ◽

Harald Binder ◽

...

Keyword(s):

Mental Health ◽

Risk Factors ◽

Systematic Review ◽

Protective Factors ◽

Health Research ◽

Large Population ◽

Risk And Protective Factors ◽

Externalizing Disorders ◽

Mental Health Research ◽

Prevalence Rates

In Germany, large, population-based cohort studies have been implemented in order to identify risk and protective factors for maintaining health across the life span. The purpose of this systematic review is to analyse findings from three large ongoing cohorts and to identify sex-specific prevalence rates, risk and protective factors for mental health. Published studies from the Cooperative Health Research in the Region Augsburg (KORA), the Study of Health in Pomerania (SHIP) and the Gutenberg Health Study (GHS)), representing the southern, north-eastern and middle parts of Germany, were identified through searches of the databases PubMed and Web of Science. A total of 52 articles was identified from the start of each cohort until June 2019. Articles reporting prevalence rates of mental health [N = 22], explanatory factors for mental health [N = 25], or both [N = 5] were identified. Consistent across cohorts, higher prevalence rates of internalizing disorders were found for women and more externalizing disorders for men. Risk and protective factors for mental health included social factors, lifestyle, physical health, body mass index (BMI), diabetes, genetic and biological factors. In all areas, differences and similarities were found between women and men. The most evident were the sex-specific risk profiles for depression with mostly external risk factors for men and internal risk factors for women. Gender was not assessed directly, therefore we examined whether socioeconomic and family-related factors reflecting gender roles or institutionalized gender could be used as a proxy for gender. Overall, this systematic review shows differences and similarities in prevalence rates and determinants of mental health indicators between women and men. They underline the importance of focussing on sex specific approaches in mental health research and in the development of prevention measures. Current research on mental health still lacks focus on gender aspects. Therefore, an increased focus on sex and gender in mental health research is of great importance.

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Measures of outer setting constructs for implementation research: A systematic review and analysis of psychometric quality

Implementation Research and Practice ◽

10.1177/2633489520940022 ◽

2020 ◽

Vol 1 ◽

pp. 263348952094002

Author(s):

Sheena McHugh ◽

Caitlin N Dorsey ◽

Kayne Mettert ◽

Jonathan Purtle ◽

Eric Bruns ◽

...

Keyword(s):

Mental Health ◽

Systematic Review ◽

Implementation Research ◽

Health Research ◽

Convergent Validity ◽

Discriminant Validity ◽

Peer Pressure ◽

Mental Health Research ◽

Patient Needs ◽

Outer Setting

Background: Despite their influence, outer setting barriers (e.g., policies, financing) are an infrequent focus of implementation research. The objective of this systematic review was to identify and assess the psychometric properties of measures of outer setting used in behavioral and mental health research. Methods: Data collection involved (a) search string generation, (b) title and abstract screening, (c) full-text review, (d) construct mapping, and (e) measure forward searches. Outer setting constructs were defined using the Consolidated Framework for Implementation Research (CFIR). The search strategy included four relevant constructs separately: (a) cosmopolitanism, (b) external policy and incentives, (c) patient needs and resources, and (d) peer pressure. Information was coded using nine psychometric criteria: (a) internal consistency, (b) convergent validity, (c) discriminant validity, (d) known-groups validity, (e) predictive validity, (f) concurrent validity, (g) structural validity, (h) responsiveness, and (i) norms. Frequencies were calculated to summarize the availability of psychometric information. Information quality was rated using a 5-point scale and a final median score was calculated for each measure. Results: Systematic searches yielded 20 measures: four measures of the general outer setting domain, seven of cosmopolitanism, four of external policy and incentives, four of patient needs and resources, and one measure of peer pressure. Most were subscales within full scales assessing implementation context. Typically, scales or subscales did not have any psychometric information available. Where information was available, the quality was most often rated as “1-minimal” or “2-adequate.” Conclusion: To our knowledge, this is the first systematic review to focus exclusively on measures of outer setting factors used in behavioral and mental health research and comprehensively assess a range of psychometric criteria. The results highlight the limited quantity and quality of measures at this level. Researchers should not assume “one size fits all” when measuring outer setting constructs. Some outer setting constructs may be more appropriately and efficiently assessed using objective indices or administrative data reflective of the system rather than the individual.

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Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.738466 ◽

2021 ◽

Vol 12 ◽

Author(s):

Mohammad Chowdhury ◽

Eddie Gasca Cervantes ◽

Wai-Yip Chan ◽

Dallas P. Seitz

Keyword(s):

Mental Health ◽

Artificial Intelligence ◽

Machine Learning ◽

Systematic Review ◽

Health Research ◽

Mental Health Research ◽

Risk Of Bias ◽

Geriatric Mental Health ◽

Electronic Health

Introduction: Electronic health records (EHR) and administrative healthcare data (AHD) are frequently used in geriatric mental health research to answer various health research questions. However, there is an increasing amount and complexity of data available that may lend itself to alternative analytic approaches using machine learning (ML) or artificial intelligence (AI) methods. We performed a systematic review of the current application of ML or AI approaches to the analysis of EHR and AHD in geriatric mental health.Methods: We searched MEDLINE, Embase, and PsycINFO to identify potential studies. We included all articles that used ML or AI methods on topics related to geriatric mental health utilizing EHR or AHD data. We assessed study quality either by Prediction model Risk OF Bias ASsessment Tool (PROBAST) or Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist.Results: We initially identified 391 articles through an electronic database and reference search, and 21 articles met inclusion criteria. Among the selected studies, EHR was the most used data type, and the datasets were mainly structured. A variety of ML and AI methods were used, with prediction or classification being the main application of ML or AI with the random forest as the most common ML technique. Dementia was the most common mental health condition observed. The relative advantages of ML or AI techniques compared to biostatistical methods were generally not assessed. Only in three studies, low risk of bias (ROB) was observed according to all the PROBAST domains but in none according to QUADAS-2 domains. The quality of study reporting could be further improved.Conclusion: There are currently relatively few studies using ML and AI in geriatric mental health research using EHR and AHD methods, although this field is expanding. Aside from dementia, there are few studies of other geriatric mental health conditions. The lack of consistent information in the selected studies precludes precise comparisons between them. Improving the quality of reporting of ML and AI work in the future would help improve research in the field. Other courses of improvement include using common data models to collect/organize data, and common datasets for ML model validation.

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