Assessing Physician Awareness of the Choosing Wisely Canada Recommendations
Bibliographic record
Abstract
Background: Choosing Wisely Canada (CWC), an initiative to reduce low-value care, launched in April 2014. However, it remains unclear to what extent physicians are aware of the initiative and specific recommendations. The objective of this study was to assess physician awareness of the CWC campaign and recommendations, in addition to assessment of attitudes and perspectives on low-value medical care. Methods: This study was conducted as a survey of faculty physicians and residents of McMaster University. Electronic surveys were sent to all faculty physicians and residents within specialties with CWC recommendations. Responses were analyzed to determine awareness of CWC recommendations, defined as awareness of ≥3 recommendations targeted to a respondent’s respective specialty. Results: A total of 361 respondents were included in the analysis (response rate = 33%). Eighty-eight percent of respondents were aware of the CWC campaign. Only 30.1% (95% CI 23.5–36.7%) of respondents were able to correctly describe ≥3 of the recommendations targeted to their respective specialty, with a mean of 1.6 (95% CI 1.4–1.9) recommendations correctly identified per respondent. Most recommendations (70.9%) were reported as already being part of a respondents’ practice prior to release of the CWC recommendations. Interpretation: Despite general awareness of the CWC campaign, more than two thirds of physicians cannot describe most recommendations targeted to their own specialty. Nonetheless, many of these physicians report already practicing in compliance with these recommendations. Future studies are required to identify methods to improve communication, to track compliance with current CWC recommendations, and to determine areas of care that would most benefit from additional 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 | 0.005 | 0.034 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".