Commitment to change statements can predict actual change in practice
Bibliographic record
Abstract
INTRODUCTION: Statements of commitment to change are advocated both to promote and to assess continuing education interventions. However, most studies of commitment to change have used self-reported outcomes, and self-reports may significantly overestimate actual performance. As part of an educational randomized controlled trial, this study documented changes that family physicians committed to make in their prescribing and then used third-party data to examine actual changes. METHOD: Following participation in a continuing medical education program using interactive small groups, physicians were asked to identify changes that they planned to make in their practices. For prescribing changes related to four conditions, data from a provincial pharmacy registry were analyzed for 6-month periods before and after the educational intervention. RESULTS: A total of 207 physicians participated in the project, which involved monthly meetings of 30 peer learning groups. Ninety-nine physicians received experimental case-based educational modules +/- personal prescribing feedback, and 91 of these indicated that they planned to make at least one change in practice. Of the 209 intended changes, 71% were directly related to the prescribing messages in the materials. DISCUSSION: In three of four indicator conditions, physicians who expressed a commitment to change were significantly more likely to change their actual prescribing for the target medications in the following 6 months. The percentage of physicians who did change their prescribing varied significantly by condition. Further study of the process of translating commitment to change into real practice change is needed.
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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 | 0.006 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".