Improving students’ mental health literacy: evaluation of an adapted school intervention in Germany
Bibliographic record
Abstract
Abstract Background Mental health problems and mental illness are central concerns for young people's health. As most mental illnesses emerge before the age of 25, young people are considered a key target group for preventive measures. Mental health literacy (MHL) is considered a set of resources and capacities for positive mental health and mental health care. MHL encompasses specific mental health knowledge, positive attitudes towards mental illness, and effective help-seeking behaviors. School-based MHL interventions seem to be promising in reaching young people and educating and empowering them regarding the topic of mental health. Methods An evidence-based Canadian MHL school intervention was adapted for German schools and evaluated for its effectiveness in terms of strengthening different dimensions of MHL (mental health knowledge, stigmatizing attitudes, help-seeking efficacy) as well as its acceptability. The evaluation study included a pre-post intervention control group design. Paired sample t-tests were conducted separately for the intervention and control group in order to verify the improvements in the different dimensions of MHL. Results The sample comprises 251 students aged Ø 15.6 years, with 9 intervention classes (IC) and 5 control classes (CC). The analysis showed significant improvements in the MHL dimensions knowledge, attitudes towards mental illness (personal stigma) and help-seeking for the IC. In addition, the students’ reception of the intervention was highly positive which reflects high acceptance. Conclusions Despite the rather small sample size (due to the COVID-19 pandemic), the evaluation offers insights into the effectiveness in terms of improving the different MHL-dimensions, and applicability of the adapted MHL intervention for students in Germany. Further evaluations are needed to confirm the intervention's effectiveness, especially with regard to long-term effects.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.003 | 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".