SMART Mental Health Project: process evaluation to understand the barriers and facilitators for implementation of multifaceted intervention in rural India
Bibliographic record
Abstract
BACKGROUND: Globally, mental health problems are a growing public health concern. Resources and services for mental disorders are disproportionately low compared to disease burden. In order to bridge treatment gaps, The Systematic Medical Appraisal, Referral and Treatment (SMART) Mental Health Project was implemented across 12 villages in West Godavari district of the southern Indian state of Andhra Pradesh. This paper reports findings from a process evaluation of feasibility and acceptability of the intervention that focused on a mental health services delivery model to screen, diagnose and manage common mental disorders (CMDs). METHODS: A mixed methods evaluation was undertaken using quantitative service usage analytics, and qualitative data from in-depth interviews and focus group discussions were conducted with stakeholders including primary care physicians, community health workers, field staff and community members. Barriers to and facilitators of intervention implementation were identified. Andersen's Behavioral Model for Health Services Use was the conceptual framework used to guide the process evaluation and interpretation of data. RESULTS: In all, 41 Accredited Social Health Activists (ASHAs) and 6 primary health centre (PHC) doctors were trained in mental health symptoms and its management. ASHAs followed up 98.7% of screen positive cases, and 81.2% of these were clinically diagnosed and treated by the PHC doctors. The key facilitators of implementation were adequate training and supervision of field staff, ASHAs and doctors, use of electronic decision support, incorporation of a door-to-door campaign and use of culturally tailored dramas/videos to raise awareness about CMDs, and organising health camps at the village level facilitating delivery of intervention activities. Barriers to implementation included travel distance to receive care, limited knowledge about mental health, high level of stigma related to mental health issues, and poor mobile network signals and connectivity in the villages. Lack of familiarity with and access to mobile phones, especially among women, to accessing health related messages as part of the intervention. CONCLUSIONS: The evaluation not only provides a context to the interventions delivered, but also allowed an understanding of possible factors that need to be addressed to make the programme scalable and of benefit to policy makers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".