Process evaluation of the systematic medical appraisal, referral and treatment (SMART) mental health project in rural India
Bibliographic record
Abstract
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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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 | 0.055 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".