Evaluating the effectiveness of a school-based mental health literacy intervention from a comprehensive demographic and social-cognitive perspective
Bibliographic record
Abstract
Childhood and adolescence are a critical period for the onset of mental and neurodevelopmental disorders and a time when many can be first identified. Research demonstrates that mental health literacy applied in school settings may be an effective approach to address these challenges. In contrast to many existing studies conducted in multicultural and multilingual settings that treated subjects' language as a demographic feature, the present study recognizes English proficiency as a social-cognitive factor and views the school-based mental health literacy (MHL) intervention as a learning process. The present study aimed to assess the effectiveness of school-based mental health literacy intervention and explore how ethnicity and English proficiency as a social-cognitive factor, as a modified, rather than a fixed variable, impacted the intervention outcomes. Grade 9 students (n = 240) from schools in West Canada with diverse social/cultural background received the intervention in the classroom delivered by trained teachers and completed the pre-test and post-test over a 6-month period. The intervention was effective in improving knowledge and help-seeking attitudes among all students. Non-Chinese and native English-speaking students performed the best on all outcomes. Gender demonstrated an association with changes in stigma, stress and wellbeing. English proficiency was linked to knowledge acquisition, while ethnicity was connected to changes of attitude-related outcomes. These findings deepened our understanding of how demographic and social-cognitive factors underlie changes in mental health literacy outcomes, which will facilitate the development of mental health literacy interventions for diverse student populations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".