Process evaluation of the Systematic Medical Appraisal, Referral and Treatment (SMART) Mental Health Project in rural India
Open accessAbstract
BACKGROUND: Availability of basic mental health services is limited in rural areas of India. Health system and individual level factors such as lack of mental health professionals and infrastructure, poor awareness about mental health, stigma related to help seeking, are responsible for poor awareness and use of mental health services. We implemented a mental health services delivery model that leveraged technology and task sharing to facilitate identification and treatment of common mental disorders (CMDs) such as stress, depression, anxiety and suicide risk in rural areas of the state of Andhra Pradesh, India. The intervention was delivered by lay village health workers (Accredited Social Health Activists - ASHAs) and primary care doctors. An anti-stigma campaign was implemented prior to this activity. This paper reports the process evaluation of the intervention using mixed methods. METHODS: A mixed methods pre-post evaluation assessed the intervention using quantitative service usage analytics from the server, and qualitative interviews with different stakeholders. Barriers and facilitators in implementing the intervention were identified. RESULTS: Health service use increased significantly at post-intervention, ASHAs could followup 78.6% of those who had screened positive, and 78.6% of the 1243 Interactive Voice Response System calls made, were successful. Most respondents were aware of the intervention. They indicated that knowledge received through the intervention empowered them to approach ASHAs and share their mental health symptoms. ASHAs and doctors opined that EDSS was useful and easy to use. Medical camps organized in villages to increase access to the doctor were received positively by all. However, some aspects or facilitators of the intervention need to be improved, including network connectivity, booster training, anti-stigma campaigns, quality of mental health services provided by doctors, provision of psychotropic medications at primary health centers and frequency of health camps. CONCLUSION: The respondents' views helped to understand the barriers and facilitators for improving the likely effectiveness of the intervention using Andersen's Modified Behavioral Model of Health Services Use, and identify the mechanisms by which those factors affected mental health services uptake in the community. TRIAL REGISTRATION: The study is registered with Clinical Trials Registry India (Applied - 16/07/14-Ref2014/07/007256; registration received - 04/10/17-CTRI/2017/10/009992 ).
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These factors limited access to interventions and the ability to fruitfully engage in programs, including difficulty or inability to travel, unavailability due to income or childcare obligations, unwillingness due to lack of monetary incentive, or inability to understand program materials.
“Oftentimes, these factors limited access to interventions and the ability to fruitfully engage in programs (e.g., difficulty or an inability to travel to intervention locations [53], an unavailability to participate due to a necessity to generate income or childcare obligations [47, 48], an unwillingness to participate due to lack of monetary incentive, or an inability in understanding program”
From Barriers and facilitators to implementation of evidence-based task-sharing mental health interventions in low- and middle-income countries: a systematic review using implementation science frameworks (2022) · cites this paper as [53]
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Experimental — accuracy not yet measuredMobile app-based clinical guidelines help improve protocol compliance.
“the available data demonstrates that mobile app-based clinical guidelines help to improve protocol compliance [24–26]”
Mobile app-based clinical guidelines can increase health workers morale.
“and can facilitate task-shifting, and increase health workers morale [25, 27].”
Mobile app-based clinical guidelines can facilitate task-shifting.
“and can facilitate task-shifting, and increase health workers morale [25, 27].”
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- Authors
- Tewari, A., Kallakuri, S., Devarapalli, S., Jha, V., Patel, A., & Maulik, P. K.
- Affiliations
- George Institute for Global Health (IN)
- The George Institute for Global Health (GB)
- UNSW Sydney (AU)
- Corresponding author
- Maulik, Pallab K.
- ORCID
- Venue
- BMC Psychiatry
- Published
- 2017-12
- Volume
- 17
- Issue
- 1
- Pages
- 385-385
- ISSN
- 1471-244X (ISSN-L)
- PMC
- PMC5715622
- OpenAlex
- W2774250304
- Cited by
- 65
- References
- 16
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 6 · 2023: 8 · 2022: 5 · 2021: 17 · 2020: 9 · 2019: 3 · 2018: 5
- FWCI
- 6.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Psychiatry (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Wellcome Trust/DBT India Alliance (IA/I/13/1/500879)
- Grand Challenges Canada (0524-01-10)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Social Sciences › Psychology › Clinical Psychology › Psychiatric care and mental health services
- Keywords
- common mental disorders, Accredited Social Health Activists, task sharing, interactive voice response, mental health screening, anti-stigma campaigns, help-seeking, mental health service utilization, barriers and facilitators, medical camps, primary care, Andersen Behavioral Model, Andhra Pradesh
- MeSH terms
- Adult, Mental Disorders, Community Health Workers, Female, Humans, India, Mental Health, Mental Health Services, Referral and Consultation, Rural Population, Social Stigma
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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.
What counts as a claim ›How we measure confidence ›How we check this ›
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