Engaging residents to choose wisely: Resident Doctors of Canada resource stewardship recommendations
Bibliographic record
Abstract
BACKGROUND: Resident doctors are integral to healthcare delivery in Canada. Engaging residents in resource stewardship is important for professional development, but also as they are drivers of healthcare resource use. To date, no national resident-specific resource stewardship guideline has been developed. Resident Doctors of Canada (RDoC) in collaboration with Choosing Wisely Canada (CWC) sought to develop an evidence-informed, consensus-based list of five recommendations to promote resource stewardship. METHODS: RDoC convened a taskforce with diverse geographic and specialty representation to develop candidate recommendations targeting resident resource stewardship behaviours using a consensus-based process, supported by a literature review. Residents across the country provided feedback on the candidate recommendations via an online questionnaire. The taskforce used this feedback to finalize the list. RESULTS: The taskforce prepared 28 candidate recommendations for consideration. A detailed literature review and consensus process narrowed this list to 12 candidate recommendations for consultation. A total of 754 residents (754/10,068 residents = 7.5%) representing all provinces and levels of residency training reviewed and ranked the candidate recommendations. The highest-ranked recommendations comprised the final list. CONCLUSION: Resident doctors are willing and able to demonstrate leadership in advancing resource stewardship by the development of a national resident-specific list of Choosing Wisely Canada recommendations.
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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 | 0.018 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".