Introducing change management education program for family medicine residents: a demonstration project
Bibliographic record
Abstract
Implication Statement: The project presents an innovative mixed learning approach program to provide basic change management training for family medicine residents. Developed by a team of faculty experts in the Department of Family Medicine at the University of Manitoba, this three-to-four-hour training program provided residents an understanding of an approach to change management that systematically plans, implements, and evaluates new initiatives in healthcare settings. Students reported that change management is important for their success as healthcare professionals. This program could easily be replicated. Énoncé des implications de la recherche: Ce projet consiste en programme novateur fondé sur une approche d'apprentissage mixte visant à offrir une formation de base en gestion du changement aux résidents en médecine familiale. Élaborée par une équipe de professeurs experts du département de médecine familiale de l'Université du Manitoba, cette formation d'une durée de trois à quatre heures a permis aux résidents de se familiariser avec une approche de la gestion du changement qui consiste à planifier, à mettre en œuvre et à évaluer systématiquement de nouvelles initiatives en milieu clinique. Les étudiants estiment que la gestion du changement est un facteur important pour leur réussite en tant que professionnels de la santé. Ce programme peut aisément être reproduit ailleurs.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".