Operational challenges in conducting a community-based technology-enabled mental health services delivery model for rural India: Experiences from the SMART Mental Health Project
Bibliographic record
Abstract
Background: There are large gaps in the delivery of mental health care in low- and middle-income countries such as India, and the problems are even more acute in rural settings due to lack of resources, remoteness, and lack of infrastructure, amongst other factors. The Systematic Medical Appraisal Referral and Treatment (SMART) Mental Health Project was conceived as a mental health services delivery model using technology-based solutions for rural India. This paper reports on the operational strategies used to facilitate the implementation of the intervention. Method: Key components of the SMART Mental Health Project included delivering an anti-stigma campaign, training of primary health workers in screening, diagnosing and managing stress, depression and increased suicide risk and task sharing of responsibilities in delivering care; and using mobile technology based electronic decision support systems to support delivery of algorithm based care for such disorders. The intervention was conducted in 42 villages across two sites in the state of Andhra Pradesh in south India. A pre-post mixed methods evaluation was done, and in this paper operational challenges are reported. Results: Both quantitative and qualitative results from the evaluation from one site covering about 5000 adults showed that the intervention was feasible and acceptable, and initial results indicated that it was beneficial in increasing access to mental health care and reducing depression and anxiety symptoms. A number of strategies were initiated in response to operational challenges to ensure smoother conduct of the project and facilitated the project to be delivered as envisaged. Conclusions: The operational strategies initiated for this project were successful in ensuring the delivery of the intervention. Those, coupled with other more systematic processes have informed the researchers to understand key processes that need to be in place to develop a more robust study, that could eventually be scaled up.
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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 | 0.030 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".