Adaptation of a french e-health tool for suicide prevention in young populations: Modalities and benefits
Bibliographic record
Abstract
Introduction France’s suicide rate is among the highest in Europe, with the young among the more at risk than others. Several European projects have demonstrated the effectiveness of using e-tools in suicide prevention particularly for hard-to-reach populations. Lessons from StopBlues, an e-health tool (application/website) for suicide prevention in the general population developed in 2018 which was promoted by municipalities and general practitioners, shows the necessity to adapt its content for young people. Objectives The objective is to develop an e-health tool, BlueZberry, for suicide prevention targeting adolescents and young adults with psychological pain by adapting StopBlues and its promotional plan. Methods The detailed content of BlueZberry and its promotional plan were determined via a literature review and 26 individual and group interviews with experts and youth with StopBlues as a starting part. Results The literature review and interviews confirmed the need to adapt the tool according to age of the user since the context and source of psychological pain vary rapidly at this time of life. BlueZberry consists of three modules for age groups 12-14, 15-17 and 18-25 years with specific graphics and messages. Its locally organized promotion should include youth hangouts on top of usual places. Conclusions This adaptation of StopBlues will reach a larger audience by offering a more suitable solution for this vulnerable population. A web-portal will serve as an entry point for both StopBlues and BlueZberry where users will be redirected to one of the tools/modules according to their profile and respective needs.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.011 | 0.004 |
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".