Commitment to change statements: A way of understanding how participants use information and skills taught in an educational session
Bibliographic record
Abstract
BACKGROUND: Commitment to change has gained increasing use in assessing short course effectiveness. This study examined the changes that learners intended to make in practice following an intensive day-long course offered at multiple sites, counted changes relative to the curriculum's focus, and analyzed which changes were implemented in practice. METHODS: Participants at a course on the management of male sexual dysfunction were asked to identify the changes to which they would commit. Six months after the course, they were asked to indicate which changes they implemented fully, partially, or not at all. RESULTS: A total of 352 physicians attended the courses held in 21 centers. A majority of attendees (344 or 97.7%) completed forms at the end of the course, providing 1,635 commitment statements. Six months later, 197 (57.3%) physicians provided follow-up data about 935 (55.4%) of the commitment statements originally submitted. Of these, 602 (66.52%) were completely implemented. Many of the changes related to two specific aspects of the course, namely, sexual history taking and medical intervention, accounting for 45.93% of the intended commitments and 47.67% of the changes completely implemented. Slightly over half (58%) of the course time was devoted to these two areas. There was a significant correlation between the number of changes and the amount of time allocated to that content within the course. FINDINGS: Commitment to change statements offered by course participants can be used to examine the impact of a course relative to its learning focus. Continuing medical education providers must take a critical look at commitment to change statements as an "intervention" in their own right and determine how the tool can best be used as a continuing medical education intervention.
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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 | 0.025 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".