The Systematic Medical Appraisal, Referral and Treatment (SMART) Mental Health Project: Development and Testing of Electronic Decision Support System and Formative Research to Understand Perceptions about Mental Health in Rural India
Bibliographic record
Abstract
INTRODUCTION: Common mental disorders (CMD) such as depression, suicidal risk and emotional/medically unexplained complaints affect a large number of people in India, but few receive appropriate care. Key reasons for this include few trained mental health professionals and stigma associated with mental health. A potential approach to address poor access to care is by training village healthcare workers in providing basic mental health care, and harnessing India's vast mobile network to support such workers using mobile-based applications. We propose an intervention to implement such an approach that incorporates the use of mobile-based electronic decision support systems (EDSS) to provide mental health services for CMD, combined with a community-based anti-stigma campaign. This will be implemented and evaluated across 42 villages in Andhra Pradesh, a south Indian state. This paper discusses the development and testing of the EDSS, and the formative research that informed the anti-stigma campaign. MATERIALS AND METHODS: The development of the EDSS used an iterative process that was validated against clinical diagnosis. A mixed methods approach tested the user acceptability of the EDSS. Focus group discussions and in-depth interviews provided community-level perceptions about mental health. This study involved 3 villages and one primary health centre. RESULTS: The EDSS application was found to be acceptable, but some modifications were needed. The community lacked adequate knowledge about CMD and its treatment and there was stigma associated with mental illness. Faith and traditional healers were considered to be important mental health service providers. DISCUSSION: A number of barriers and facilitators were identified in implementing the intervention analysed in a framework using Andersen's behavioural model of health services use. CONCLUSION: The findings assisted with refining the intervention prior to large-scale implementation and evaluation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.130 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".