P033: To choose or not to choose: evaluating the impact of a Choosing Wisely knowledge translation initiative on urban and rural emergency physician guideline awareness
Bibliographic record
Abstract
Introduction: Choosing Wisely is an innovative approach to address physician and patient attitudes towards low value medical tests; however, a knowledge translation (KT) gap exists. We aimed to quantify the baseline familiarity of emergency medicine (EM) physicians with the Choosing Wisely Canada (CWC) EM recommendations. We then assessed whether a structured KT initiative affected knowledge and awareness. Methods: Physicians working in urban (tertiary teaching hospital, Saint John, NB) and rural (community teaching hospital, Waterville, NB) emergency departments were asked to participate in a survey assessing awareness and knowledge of the first five CWC EM recommendations before an educational intervention. The intervention consisted of a 1-hour seminar reviewing the recommendations, access to a video cast and departmental posters. Knowledge was assessed by asking respondents to identify 80% or more of the recommendations correctly. Physicians were surveyed again at a 6-month follow up period. The Fisher exact test was used for statistical analyses. A sample size of 36 was required to detect a 30% change with an alpha of 0.05 and a power of 80%. Results: At the urban site, 16 of 25 (64%) physicians responded to the pre- and 14 of 26 (53.8%) responded to the post-intervention survey. Awareness of the EM recommendations did not increase significantly (81.3% pre; 95% CI 56.2-94.2 vs. 92.9% post; 66.4-99.9; p=0.60). There was a weak trend towards improved knowledge with 62.5% (38.5-81.6) of physicians responding correctly initially, and 85.7% (58.8-97.2; p=0.23) after the intervention. At the rural site, 8 of 11 (72.7%) physicians responded to the pre- and post-intervention survey. There was a trend towards improved awareness, (25% pre; 6.3-59.9 vs. 75% post; 40.1-93.7; p=0.13), with 50% (21.5-78.5) responding correctly pre, and 87.5% (50.8-99.9; p=0.28) after the intervention. Conclusion: We have described the current awareness and knowledge of the CWC EM recommendations. Limited by our small sample size, we report a trend towards increased awareness and knowledge at 6 months following our KT initiative in a rural setting where there was a low baseline awareness. At the urban site where baseline knowledge was high, changes seen were less significant. Further work will look at the effectiveness of our initiative on physician practice.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".