Commitment to change instrument enhances program planning, implementation, and evaluation
Bibliographic record
Abstract
INTRODUCTION: This study investigates the use of a commitment to change (CTC) instrument as an integral approach to continuing medical education (CME) planning, implementation, and evaluation and as a means of facilitating physician behavior change. METHODS: Descriptive statistics and grounded theory methods were employed. Data were collected from 20 consecutive CME programs. Physicians were asked to list up to three things they intended to change in their clinical practice as a result of the program. A copy was sent 3 weeks later as a reminder. Six months later, a summary of peer-intended changes was sent to reinforce intended behavior change. RESULTS: Of 602 participants, 291 (48%) completed CTC forms, resulting in 803 citations. Responses were congruent with the educational objectives and intentions of the program planners. Using the constant comparative method of analysis, a framework was identified for interpreting physician learning strategies. It included change strategies and motivation, learning issues, better doctoring, changes to clinic practice, and diffusion. DISCUSSION: CTC was useful as a multipurpose tool providing planners with meaningful feedback to (1) assess congruence of intended changes in physician behavior with program objectives, (2) document unanticipated learning outcomes, and (3) enable and reinforce intended behavior change.
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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 | 0.061 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".