Processus novateur pour favoriser la pérennisation et la mise à l’échelle de programmes de prévention de l’anxiété à l’école : l’exemple du programme HORS-PISTE
Bibliographic record
Abstract
Context Anxiety disorders are among the most prevalent psychopathologies for children and adolescents in Quebec. The prevalence of anxiety disorders is very high and has been affecting a growing number of young people for the past 10 years. It is possible to observe an increased number of anxiety prevention programs for young people around the world. However, some authors point out that they are rarely faithfully implemented, sustained, and scaled up in several schools. Based on implementation science, this HORS-PISTE program was developed to address these important issues by preventing anxiety in Quebec high school students. Implemented in more than 100 schools, the program is now part of Action 4.3 (Promote the deployment of the HORS-PISTE program) of the new interdepartmental Action Plan on Mental Health of the Government of Quebec (2022). Purpose This article aims to describe how the Knowledge-to-Action (KTA) framework, derived of implementation science, was used to design, implement, sustain, evaluate, and scale up the HORS-PISTE program. This framework proposes a cyclical process in seven phases. Method A multi-method and multi-stakeholder approach was conducted with a grant from the Public Health Agency of Canada's Mental Health Promotion Innovation Fund, which has been supporting 20 innovative projects across Canada since 2019. It includes a pre-post evaluation protocol consisting of validated questionnaires, surveys (administered to students, parents, and teachers), semi-structured logbooks completed by program facilitators and implementation review meetings in each school. The different cycles of the program development, implementation and evaluation are discussed through the KTA framework phases. Results From 2017 to 2021, this methodology made it possible to evaluate and readjust the program each year to promote its adaptation and prepare its scaling up. This article highlights the data collected and analyzed in relation to the seven phases of the KTA framework. Conclusion This article demonstrates how implementation science can support designers of anxiety prevention programs who are concerned by scaling up and sustaining their programs. Issues in combining the scientific rigor of evaluation with the reality of the field are also raised.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.087 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".