Integrating Exercise Counseling Into the Medical School Curriculum: A Workshop-Based Approach Using Behavior Change Techniques
Bibliographic record
Abstract
Objective. Physician physical activity (PA) counseling remains low due partly to lack of knowledge, emphasizing the importance of providing learning opportunities to develop competency, given the strong associations between PA and health. This study aimed to describe the behavior change techniques (BCTs) used in an “Exercise Expo” workshop and examine the workshop’s effectiveness for improving social cognitions to discuss exercise with patients. Methods. Second-year medical students (N = 54; M age ± SD = 25.4 ± 2.95 years) completed questionnaires assessing attitudes, perceived behavior control (PBC), subjective norms, and intentions to provide PA counseling pre- and postworkshop. Repeated-measures analyses of variance evaluated changes in these theory of planned behavior constructs. Results. The most used BCTs included presenting information from credible sources, with opportunities for practicing the behavior and receiving feedback. Significant increases in attitudes, PBC and intentions to discuss PA were observed from pre-post Exercise Expo ( P ≤ .01). No statistically significant differences in subjective norms were observed ( P = .06). Conclusions. The Exercise Expo significantly improved social cognitions for PA counseling among medical students. Future interventions should target improvements in subjective norms to increase the likelihood the workshop improves PA counseling behavior. The evidence supports the usefulness of a workshop-based educational strategy to enhance medical students’ social cognitions for PA counseling.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".