Resource stewardship learning needs in undergraduate medical training: a cross-sectional study
Bibliographic record
Abstract
Background: No clear guidelines for Canadian medical schools regarding the integration of resource stewardship (RS) in the training of tomorrow's physicians exist. This study aimed to determine the learning needs of undergraduate medical students on RS in Quebec. Methods: , 2021. A cross-sectional study was then performed. In total, 900 medical students (23.2% of all Quebec medical students) were included in this study. Results: RS teaching was perceived as being insufficient. Despite 69% of students knowing the existence of Choosing Wisely, only 21.7% considered RS teaching to sufficiently develop their critical thinking. While students claimed that RS's current teaching methods are predominantly theoretical, a preference for more practical learning approaches was noted. Approximately 71% of students undergoing clerkship rotations acknowledged the significance of role models in fostering critical thinking skills related to RS. Heterogeneity was noted across medical schools. Conclusions: This study confirms that according to students' perspective, RS is important yet insufficiently and heterogeneously taught in Quebec. Establishing guidelines regarding RS teaching in medical schools could provide equal learning opportunities. Furthermore, encouraging role models to be part of this teaching effort may also strengthen student's critical thinking regarding RS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".