Choosing Wisely for Medical Education: Six Things Medical Students and Trainees Should Question
Bibliographic record
Abstract
PROBLEM: Physician behaviors that promote overuse of health care resources develop early in training, and the medical education environment helps foster such behaviors. The authors describe the development of a Choosing Wisely list for medical students aimed at helping to curb overuse. APPROACH: The list was developed in 2015 by Choosing Wisely Canada (CWC) in partnership with the Canadian Federation of Medical Students and the Fédération médicale étudiante du Québec, which together represent all medical students in Canada. CWC convened a student-led taskforce to develop recommendations targeting medical student behaviors with respect to resource stewardship practices. Students at all 17 Canadian medical schools were consulted via an online questionnaire to solicit feedback on a list of 10 candidate recommendations. The taskforce used this student feedback in finalizing the list. OUTCOMES: The final list of "Six Things That Medical Students and Trainees Should Question" highlights both behaviors students should avoid (e.g., "Don't suggest ordering the most invasive test before considering other less invasive options") and behaviors related to aspects of medical training that may promote overuse, such as the hierarchical nature of clinical supervision (e.g., "Don't hesitate to ask for clarification on tests, treatments, or procedures that you believe may be ordered inappropriately"). Based on student requests for illustrative examples, clinical vignettes were developed. NEXT STEPS: This list highlights medical student behaviors and aspects of the academic environment that drive overuse. It is also relevant to faculty, whose behaviors and supervision practices influence trainees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".