Evaluation of a school-based intervention to promote mental health literacy. Preliminary results
Bibliographic record
Abstract
Abstract Background Mental illness is one of the leading causes of ill-health and disability. The current COVID-19 pandemic poses an additional challenge on mental health, e.g., through increased levels of anxiety, depression and fears about the future. As most mental illnesses develop before the age of 25, young people are a critical target group for prevention. Mental health literacy (MHL) is considered to be a key determinant of positive mental health, thus, being crucial for mental health promotion and dealing with the aftermath of the pandemic. This project aims at (i) adapting an evidence-based MHL curriculum for German schools, as school-based interventions are suitable in reaching most young people, and (ii) evaluating its acceptance and effectiveness regarding MHL and stigmatizing attitudes. Methods An interdisciplinary team including representatives from self-help groups for mental health translated and adapted the Canadian “TeenMentalHealth” curriculum for German schools. The evaluation design includes a pre, post, follow-up assessment of MHL, stigmatizing attitudes, and help-seeking efficacy. Results For better applicability of the curriculum, the original program was shortened, while keeping its core contents such as knowledge and myths around mental health and illness. Written and digital material was developed and distributed to teachers, which were trained to deliver the intervention. Preliminary results show high acceptability, a significant increase in MHL and a decrease in stigmatizing attitudes, while help-seeking efficacy remained unchanged. Conclusions An MHL intervention was successfully adapted for the German school setting. While evaluation is ongoing, first results indicate its effectiveness in promoting MHL among students, decreasing stigmatizing attitudes and high acceptability, even during the times of the pandemic. Further evaluation is necessary to confirm the preliminary findings and to gain insight into the missing effect on help-seeking efficacy.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".