Effectiveness of a mental health literacy module on stigma related mental health knowledge and behaviour among youth in two educational settings in Chennai, South India: A quasi-experimental study
Bibliographic record
Abstract
BACKGROUND: Improving mental health literacy (MHL) can reduce stigma towards mental illness, decreasing delays in help-seeking for mental disorders such as psychosis. We aimed to develop and assess the impact of an interactive MHL intervention on stigma related mental health knowledge and behaviour (SRMHKB) among youth in two urban colleges in South India. METHODS: Incorporating input from stakeholders (students, teachers, and mental health professionals), we developed a mental health literacy module to address SRMHKB. The module was delivered as an interactive session lasting 90 min. We recruited 600 (300 males; 300 females; mean age 19.6) participants from two city colleges in Chennai from Jan-Dec 2019 to test the MHL module. We assessed SRMHKB before the delivery of the MHL intervention, immediately after, and at 3 and 6 months after the intervention using the Mental Health Knowledge Schedule (MAKS) and Reported and Intended Behaviour Scale (RIBS). We used generalised estimating equations (GEE) to assess the impact of the intervention over time. RESULTS: Compared to baseline, there was a statistically significant increase in stigma related knowledge and behaviour immediately after the intervention (coefficient=3.8; 95% CI: 3.5,4.1) and during the 3-month (coefficient=3.4; 95% CI: 3.0,3.7) and 6-month (coefficient=2.4; 95% CI: 2.0,2.7) follow-up. CONCLUSION: Preliminary findings suggest that a single 90-minute MHL interactive session could lead to improvements in SRMHKB among youth in India. Future research might utilise randomised controlled trials to corroborate findings, and explore how improvements can be sustained over the longer-term.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".