Élaboration, implantation et évaluation d’une formation à l’enseignement de l’offre active pour les professeurs et formateurs d’université
Bibliographic record
Abstract
Les professeurs et les formateurs des programmes de formation dans les domaines de la santé et des services sociaux ont la responsabilité d’enseigner le concept d’offre active, ses enjeux et les comportements qui y sont associés aux futurs professionnels de ces secteurs qui travailleront auprès des populations francophones vivant en contexte minoritaire. Cet article porte sur les résultats d’une recherche-développement visant l’élaboration, l’implantation et l’évaluation d’une formation à l’enseignement de l’offre active destinée aux professeurs et aux formateurs de ces domaines. Le programme de formation mis en place sera présenté et les pistes d’amélioration découlant de son évaluation seront proposées. Finalement, la discussion soulignera l’importance de facteurs tels que le temps, l’engagement et le soutien de l’administration, la souplesse, le partage des expériences, le soutien du formateur ainsi que les perspectives d’avenir lors de l’élaboration d’un tel programme.
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 | 0.062 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".