Longitudinal assessment of an anti-stigma campaign related to common mental disorders in rural India
Bibliographic record
Abstract
BACKGROUND: Stigma related to mental health and lack of trained mental health professionals is a major cause for an increased treatment gap, particularly in rural India. The Systematic Medical Appraisal, Referral and Treatment (SMART) Mental Health project delivered a complex intervention involving task sharing, an anti-stigma campaign and use of technology-based, decision-support tools to empower primary care workers to identify and manage depression, anxiety, stress and suicide risk.AimsThe aim of this article is to report changes in stigma perceptions over three time points in the rural communities where the anti-stigma campaign was conducted. METHOD: A multimedia-based anti-stigma campaign was conducted over a 3-month period in the West Godavari district of Andhra Pradesh, India. Following that, the primary care-based mental health service was delivered for 1 year. The anti-stigma campaign was evaluated in two villages and data were captured at three time points over a 24-month period (N = 1417): before and after delivery of the campaign and after completion of the health services delivery intervention. Standardised tools captured data on knowledge, attitude and behaviour towards mental health as well as perceptions related to help seeking for mental illnesses. RESULTS: Most knowledge, attitude and behaviour scores improved over the three time points. Overall mean scores on stigma perceptions related to help seeking improved by -0.375 (minimum/maximum of -2.7/2.4, s.d. 0.519, P < 0.001) during this time. Loss to follow-up was 10%. CONCLUSIONS: The data highlight the positive effects of an anti-stigma campaign over a 2-year period.Declaration of interestNone.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".