Choosing Wisely: Resource Stewardship Education in Canadian Medical Schools
Bibliographic record
Abstract
AbstractResource stewardship has important implications in terms of healthcare costs as well as patient safety. Currently, there is limited formal teaching in the undergraduate curriculum in Canadian medical schools that addresses this topic. Recently, Choosing Wisely Canada has launched a student campaign to integrate Choosing Wisely concepts into the curriculum, hoping to challenge medical students into thinking about value-based healthcare and patient safety. This article intends to highlight need for resource stewardship education, including associated test costs and its impact on the healthcare system. The strategies currently being implemented at the University of Ottawa will also be discussed. RésuméL’intendance des ressources a d’importantes répercussions sur les coûts liés aux soins de santé, ainsi que sur la sécurité des patients. À l’heure actuelle, le curriculum de premier cycle des écoles de médecine canadiennes comporte peu d’enseignement formel à ce sujet. Récemment, Choisir avec soin (version francophone de « Choosing Wisely Canada ») a lancé une campagne étudiante pour intégrer les concepts de Choisir avec soin au curriculum, dans l’espoir d’encourager les étudiants en médecine à réfléchir aux soins de santé et à la sécurité des patients fondés sur la valeur. Cet article cherche à souligner l’importance de la formation sur l’intendance des ressources, incluant les coûts associés aux examens et leur impact sur le système de soins de santé. Les stratégies mises en œuvre à l’Université d’Ottawa à l’heure actuelle seront également présentées.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".