An integrated community and primary healthcare worker intervention to reduce stigma and improve management of common mental disorders in rural India: protocol for the SMART Mental Health programme
Bibliographic record
Abstract
BACKGROUND: Around 1 in 7 people in India are impacted by mental illness. The treatment gap for people with mental disorders is as high as 75-95%. Health care systems, especially in rural regions in India, face substantial challenges to address these gaps in care, and innovative strategies are needed. METHODS: We hypothesise that an intervention involving an anti-stigma campaign and a mobile-technology-based electronic decision support system will result in reduced stigma and improved mental health for adults at high risk of common mental disorders. It will be implemented as a parallel-group cluster randomised, controlled trial in 44 primary health centre clusters servicing 133 villages in rural Andhra Pradesh and Haryana. Adults aged ≥ 18 years will be screened for depression, anxiety and suicide based on Patient Health Questionnaire (PHQ-9) and Generalised Anxiety Disorders (GAD-7) scores. Two evaluation cohorts will be derived-a high-risk cohort with elevated PHQ-9, GAD-7 or suicide risk and a non-high-risk cohort comprising an equal number of people not at elevated risk based on these scores. Outcome analyses will be conducted blinded to intervention allocation. EXPECTED OUTCOMES: The primary study outcome is the difference in mean behaviour scores at 12 months in the combined 'high-risk' and 'non-high-risk' cohort and the mean difference in PHQ-9 scores at 12 months in the 'high-risk' cohort. Secondary outcomes include depression and anxiety remission rates in the high-risk cohort at 6 and 12 months, the proportion of high-risk individuals who have visited a doctor at least once in the previous 12 months, and change from baseline in mean stigma, mental health knowledge and attitude scores in the combined non-high-risk and high-risk cohort. Trial outcomes will be accompanied by detailed economic and process evaluations. SIGNIFICANCE: The findings are likely to inform policy on a low-cost scalable solution to destigmatise common mental disorders and reduce the treatment gap for under-served populations in low-and middle-income country settings. TRIAL REGISTRATION: Clinical Trial Registry India CTRI/2018/08/015355 . Registered on 16 August 2018.
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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) | 0.001 | 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".