Conception d’une formation interdisciplinaire à la collaboration interprofessionnelle en santé et services sociaux en partenariat patient: contribution d’une approche capacitante renforcée par le design pédagogique
Bibliographic record
Abstract
Interprofessional education (IPE) has been a key area of research and teaching practice for the past twenty years in both university and practice settings in the health sciences and social psychology. IPE training opportunities arise when students from multiple health professions interactively learn together about interprofessional collaboration and how to improve care outcomes for the community at large. It has been proven that the design of care is of higher quality when healthcare professionals understand each other's respective roles, facilitating their communication and teamwork. However, this type of pedagogical approach to interdisciplinary training is fraught with problems, such as communication barriers, synchronization of schedules and logistics, as well as the compartmentalization of professions, which can lead to prejudice despite the educational efforts made. What's more, these courses are attended by large cohorts of initial university trainees, and their pedagogical design may lack authentic anchors, thus diminishing the ability of individuals to mobilize interprofessional collaboration in care partnerships. This article proposes a theoretical model based on an enhanced capability approach, including the use of pedagogical design for student success, to design this type of training by overcoming.
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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 | 0.032 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".