FAMILY PHYSICIAN BEHAVIOURS WHEN IMPLEMENTING PHYSICAL ACTIVITY PRESCRIPTIONS
Bibliographic record
Abstract
We provided structured workshops on the development and delivery of exercise prescriptions (STEP), as part of an accredited family physician (FP) CME program. PURPOSE To assess participating FPs' current practice, knowledge and barriers related to the promotion of exercise in family practice. METHODS FPs completed a questionnaire detailing exercise counseling habits, measured using a 5-point Likert scale, one year after receiving the program. Outcomes of interest were i) current counseling habits and knowledge, ii) advice and iii) perceived barriers to promoting exercise. RESULTS Thirty-four FPs responded to the questionnaire. Respondents were mostly male (67.6%) and half were between the ages of 41–50 years. The majority (64.7%) of respondents had practiced primary care for more than 10 years and 97.1% practiced in a private office/clinic setting. i) Habits & Knowledge All FPs reported often or always using verbal information to promote exercise, whereas only 8.8% often used written information in consultation. Most (67.6%) FPs felt confident giving general advice about exercise, however only 23.5% felt confident in providing specific exercise advice. Only 26.5% were familiar with recommended guidelines (ACSM and Canada's Physical Activity Guide) related to the development of an exercise prescription. ii) Advice 44.1% advised patients to exercise 3 times per week or most days. To gauge activity intensity, 35.3% used 60–85% maximum heart rate and 32.4% used the talk test or moderate intensity. Half of the FPs recommended walking for fitness and 79.4% recommended a duration of 30 minutes of aerobic exercise. iii) Barriers These included lack of time (38.2%), poor reimbursement (26.5%), inadequate prescription tools (17.6%), and inadequate counseling skills (14.7%). CONCLUSION While all FPs used verbal advice for the promotion of exercise, few utilized guidelines or written advice. Supported by CIHR.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".