Integrating physical activity prescription pads in primary care: A longitudinal qualitative study of general practitioners' experiences
Bibliographic record
Abstract
The importance of physical activity (PA) in preventing and treating many chronic diseases is undeniable. To help general practitioners (GPs) encourage patients to initiate and maintain a physically active lifestyle, 'Exercise is Medicine' has created PA prescription pads. However, they are not widely used in Canada and the reasons for this remain poorly understood. To explore the reasons and identify solutions, we interviewed 11 GPs on four times over a 1-year period after providing them with PA prescription pads. We analyzed the transcribed interviews using thematic coding to identify themes related to GPs' experiences in writing PA prescriptions. Although GPs recognized that PA has an important role in preventing and treating many chronic diseases, their use of the PA prescription pads was inconsistent. They described that it was largely influenced by: their beliefs about PA and prescribing it (e.g., PA benefits, prioritization of PA, habits, effectiveness of PA prescriptions); the environment (e.g., presence of PA prescription pads, types of pads); patients' characteristics and receptiveness to PA prescriptions (e.g., whether or not patients would change). Based on our results, we suggest that simply providing PA prescription pads and having GPs understand that PA can help prevent and treat many chronic diseases is not sufficient to get GPs to prescribe PA. Researchers should examine if providing training regarding PA prescription and creating an environment that reinforces PA prescriptions (e.g., placing PA prescriptions in patients charts for GPs, having signs to act as cues for GPs) increases the use of PA prescription pads.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".