The impact on medical practice of commitments to change following CME lectures: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Self-reported commitment to change (CTC) could be a potentially valuable method to address the need for continuing medical education (CME) to demonstrate clinical outcomes. AIM: This study determines: (1) are clinicians who make CTCs more likely to report changes in their medical practices and (2) do these changes persist over time? METHODS: Intervention participants (N = 80) selected up to three commitments from a predefined list following the lecture, while control participants (N = 64) generated up to three commitments at 7 days post-lecture. At 7 and 30 days post-lecture, participants were queried if any practice change occurred as a result of attending the lecture. RESULTS: About 91% of the intervention group reported practice changes consistent with their commitments at 7 days. Only 32% in the control group reported changes (z = 7.32, p < 0.001). At 30 days, more participants in the intervention group relative to the control group reported change (58% vs. 22%, z = 3.74, p < 0.01). Once a participant from either group made a commitment, there were no differences in reported changes (63% vs. 67%, z = <0.00, p = 0.38). CONCLUSION: Integration of CTC is an effective method of reinforcing learning and measuring outcomes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| 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".