Evaluation of an anti-stigma campaign related to common mental disorders in rural India: a mixed methods approach
Bibliographic record
Abstract
BACKGROUND: Stigma related to mental health is a major barrier to help-seeking resulting in a large treatment gap in low- and middle-income countries (LMIC). This study assessed changes in knowledge, attitude and behaviour, and stigma related to help-seeking among participants exposed to an anti-stigma campaign. METHOD: The campaign, using multi-media interventions, was part of the SMART Mental Health Project, conducted for 3 months, across 42 villages in rural Andhra Pradesh, in South India. Mixed-methods evaluation was conducted in two villages using a pre-post design. RESULTS: A total of 1576 and 2100 participants were interviewed, at pre- and post-intervention phases of the campaign. Knowledge was not increased. Attitudes and behaviours improved significantly (p < 0.01). Stigma related to help-seeking reduced significantly (p < 0.05). Social contact and drama were the most beneficial interventions identified during qualitative interviews. CONCLUSION: The results showed that the campaign was beneficial and led to improvement of attitude and behaviours related to mental health and reduction in stigma related to help-seeking. Social contact was the most effective intervention. The study had implications for future research in LMIC.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".