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Mobilizing Community Health Workers to Address Mental Health Disparities for Underserved Populations: A Systematic Review
Open accessAbstract
This systematic review evaluates efforts to date to involve community health workers (CHWs) in delivering evidence-based mental health interventions to underserved communities in the United States and in low- and middle-income countries. Forty-three articles (39 trials) were reviewed to characterize the background characteristics of CHW, their role in intervention delivery, the types of interventions they delivered, and the implementation supports they received. The majority of trials found that CHW-delivered interventions led to symptom reduction. Training CHWs to support the delivery of evidence-based practices may help to address mental health disparities. Areas for future research as well as clinical and policy implications are discussed.
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“However, few studies have worked with CHWs to provide a psychosocial intervention tailored to minority and immigrant elders in the US (38).”
From Effectiveness of a Disability Preventive Intervention for Minority and Immigrant Elders: The Positive Minds-Strong Bodies Randomized Clinical Trial (2019) · cites this paper as [38]
Experimental — accuracy not yet measuredIn the citing paper
“Digital interventions appear to have been effective for training community health workers in general health care delivery (Agarwal et al., 2015), though use of digital technologies for training community health workers to deliver psychological interventions, especially in settings in rural India, has received less attention (Barnett et al., 2018; Borkum et al., 2014; Modi et al., 2017; Naslund et”
From Acceptability and feasibility of digital technology for training community health workers to deliver brief psychological treatment for depression in rural India (2019) · cites this paper as Barnett et al., 2018
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Reframing implementation science to address inequities in healthcare delivery
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Metadata
- Authors
- Barnett, M. L., Gonzalez, A., Miranda, J., Chavira, D. A., & Lau, A. S.
- Affiliations
- University of California, Santa Barbara (US)
- University of California, Los Angeles (US)
- Corresponding author
- Barnett, Miya L.
- Venue
- Administration and Policy in Mental Health and Mental Health Services Research
- Published
- 2017-07-20
- Volume
- 45
- Issue
- 2
- Pages
- 195-211
- ISSN
- 0894-587X (ISSN-L)
- 1573-3289
- PMC
- PMC5803443
- OpenAlex
- W2739197041
- Cited by
- 431
- References
- 93
- Citations by year
- 2026: 51 · 2025: 69 · 2024: 60 · 2023: 57 · 2022: 63 · 2021: 57 · 2020: 39 · 2019: 25 · 2018: 10
- FWCI
- 3.12
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Administration and Policy in Mental Health and Mental Health Services Research (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute of Mental Health (K01MH110608)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Mental Health Treatment and Access
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Cultural Competency in Health Care
- Keywords
- community health workers, mental health disparities, underserved populations, evidence-based practice, CHW training, implementation support, symptom relief, low- and middle-income countries, United States
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Community Health Services, Female, Health Policy, Humans, Male, Mental Health Services, Middle Aged, United States, Vulnerable Populations, Healthcare Disparities, Evidence-Based Practice, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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