Development of an implementation plan for a school-based multimodal approach for depression and suicide prevention in adolescents
Bibliographic record
Abstract
Strong Teens and Resilient Minds (STORM) is a multimodal, school-based approach for depression and suicide prevention in adolescents that is currently implemented in a region in the Netherlands. The STORM approach will be implemented in new regions in the coming years. This study used the implementation mapping protocol to report on the development of the STORM implementation plan. First, a needs assessment was conducted through semi-structured interviews with stakeholders and brainstorming sessions with regional programme leaders in the two regions that started implementing STORM in 2023. This led to the identification of six main barriers to implementation: high level of demands for schools, insufficient understanding of the programme content, insufficient network collaboration, no perceived relative advantage of STORM by stakeholders, lack of attention to sustainability, and high work pressure. Second, performance and change objectives were formulated based on these barriers. For example, a performance objective for potential providers was that they felt supported by STORM. Third, implementation strategies were selected from theory and translated into practical applications through brainstorming sessions with programme leaders. The following strategies were included in the implementation plan: collaborate with similar initiatives within the region, free up time for STORM tasks, tailor strategies, identify and prepare STORM champions, and promote network weaving. Last, a plan to evaluate the implementation of STORM and the application of the STORM implementation plan was formulated. Planned evaluation research will provide more insight into the usefulness and impact of the STORM implementation plan.
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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 | 0.057 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".