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
Resume L’intervention precoce pour la psychose n’est pas encore bien implantee dans les pays francophones. Plusieurs defis surgissent dans les processus d’implantation a grande echelle, dont la fidelite aux composantes essentielles du modele deja bien identifiees et appuyees sur des donnees scientifiques. A travers la francophonie, l’exemple de la province de Quebec (Canada), permet de faire ressortir des pistes de solutions favorisant l’implantation des meilleures pratiques en intervention precoce. La collaboration entre l’Association quebecoise des programmes pour premiers episodes psychotiques (une communaute de pratique qui offre du mentorat et de la formation specialisee continue – webinaires1, cours, conferences) en complementarite avec le Centre national d’excellence en sante mentale, en plus des investissements gouvernementaux dedies et la publication d’un cadre de reference national sont des facteurs cles de succes.
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 | 0.007 | 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".