[no title]
Bibliographic record
Abstract
INTRODUCTION: Mobilizing different types of expertise helps to meet some of the challenges faced by health care systems. In French-speaking Switzerland, patient expertise has been mobilized in a new postgraduate curriculum, the Certificate of Advanced Studies (CAS) in Care Coordination and Networking, according to the different levels of engagement described in the Montreal Model. PURPOSE OF THE RESEARCH: The aim of our research was to explore a) the feasibility and acceptability of implementing different levels of patient involvement in this continuing education program, b) the factors that influence them, and c) the preliminary benefits of this implementation for students. Mixed methods were used for this exploratory study conducted between 2021 and 2022. RESULTS: All the levels of patient engagement in teaching proposed by the Montreal Model were implemented. Among the success factors, the involvement and reflective skills of the patient partners are essential. However, human, organizational, and financial resources need to be allocated to ensure the sustainability of this curriculum. Partnership seems to be a lever for organizational transformation, for the development of professional skills, and for practice improvement. CONCLUSIONS: To our knowledge, this exploratory study reflects a pedagogical and organizational innovation in the context of continuing education in French-speaking Switzerland. The results will be used to adjust the curriculum in its next edition, to disseminate it in other training contexts, to improve systemic elements to support the patient partnership in education, and to develop research.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.221 | 0.058 |
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".