Dos and Don'ts in Designing School-Based Awareness Programs for Suicide Prevention
Bibliographic record
Abstract
Abstract. Background: Despite the promising evidence for the effectiveness of school-based awareness programs in decreasing the rates of suicidal thoughts and suicide attempts in young people, no guidelines on the targets and methods of safe and effective awareness programs exist. Aims: This study intends to distill recommendations for school-based suicide awareness and prevention programs from experts. Method: A three-stage Delphi survey was administered to an expert panel between November 2018 and March 2019. A total of 214 items obtained from open-ended questions and the literature were rated in two rounds. Consensus and stability were used as assessment criteria. Results: The panel consisted of 19 participants in the first and 13 in the third stage. Recommended targets included the reduction of suicide attempts, the enhancement of help-seeking and peer support, as well as the promotion of mental health literacy and life skills. Program evaluation, facilitating access to healthcare, and long-term action plans across multiple levels were among the best strategies for the prevention of adverse effects. Limitations: The study is based on opinions of a rather small number of experts. Conclusion: The promotion of help-seeking and peer support as well as facilitating access to mental health-care utilities appear pivotal for the success of school-based awareness programs.
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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 | 0.034 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".