Are we adequately preparing the next generation of physicians to prescribe exercise as prevention and treatment? Residents express the desire for more training in exercise prescription
Bibliographic record
Abstract
BACKGROUND: Physical activity (PA) is a key intervention for chronic disease, yet few physicians provide exercise prescription (EP). EP is an important component in larger strategies of reducing non-communicable disease (NCD). Our objective was to assess Family Medicine Residents (FMR) knowledge, competence, and perspectives of EP to help inform future curriculum development. METHODS: A 49-item cross-sectional survey was administered to 396 University of British Columbia FMR. Residents' EP knowledge, competence, attitudes/beliefs, current practices, personal physical activity levels, and perspectives of training were assessed using, primarily, a 7-point Likert scale. RESULTS: The response rate was 80.6% (319/396). After eliminating 25 that failed to meet the inclusion criteria, 294 were included in the final analysis. The majority 95.6% of FMR reported EP as important in their future practice, despite having low knowledge of the Canadian PA Guidelines (mean score 1.77/4), low self-reported competence prescribing exercise as prevention (mean score 13.35/21), and rating themselves "somewhat incompetent" prescribing exercise to patients with chronic disease (mean score 11.26/21). FMR believe PA is integral to their patients' health (98.0%), sedentary behaviour is harmful (97.9%), and feel a responsibility to discuss PA with patients (99.7%). Few FMR (14.9%) perceived their training in EP as adequate and 91.0% desire more. CONCLUSIONS: FMR report EP is important, yet do not perceive they are sufficiently prepared to provide EP. In future curricular development, medical educators should consider residents' low knowledge, competence, perceived program support, and their expressed desire for more training in exercise prescription.
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".