Éléments essentiels pour l’implantation à grande échelle de programmes d’intervention précoce pour premiers épisodes psychotiques en francophonie : l’exemple du Québec
Bibliographic record
Abstract
Essential elements for the large-scale implantation of early intervention programs for first episode psychosis in the French-speaking world: The example of Quebec. Early intervention for psychosis services (EIS) has yet to be successfully implemented in a widespread manner across French-speaking countries and regions of the world. Several challenges have been met during this process. One of these challenges is the implementation of programs that adhere to the essential components of the evidence-based model of EIS. The province of Québec, in Canada, provides a prime example of such challenges and offers insight for possible solutions to ensure that EIS follows practice guidelines. In Québec, the collaboration between the Association Québécoise des programmes pour premiers épisodes psychotiques (a community of practice that offers continuing medical education and mentorship to mental health practitioners in early intervention for psychosis through webinars, courses, and conferences) and the Centre national d’excellence en santé mentale have been key elements towards providing a framework for EIS to ensure that the best practices are in place. Furthermore, new governmental investments specifically for the creation of new EIS and the improvement of already existing ones, accompanied by the publication of an administrative framework, have contributed equally towards promoting successful widespread implementation of EIS.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".