Longitudinal qualitative study describing family physicians’ experiences with attempting to integrate physical activity prescriptions in their practice: ‘It’s not easy to change habits’
Bibliographic record
Abstract
OBJECTIVE: Physical activity (PA) prescriptions provided by family physicians can promote PA participation among patients, but few physicians regularly write PA prescriptions. The objective of this study was to describe family physicians' experiences of trying to implement written PA prescriptions into their practice. DESIGN: Longitudinal qualitative study where participants were interviewed four times during a 12-month period. After the first interview, they were provided with PA prescription pads. Data were analysed using thematic analysis. SETTING: Family medicine clinics in New Brunswick, Canada. PARTICIPANTS: Family physicians (n=11) with no prior experience writing PA prescriptions, but who expressed interest in changing their practice to implement written PA prescriptions. RESULTS: Initially, participants exhibited confidence in their ability to write PA prescriptions in the future and intended to write prescriptions. However, data from the follow-up interviews indicated that the rate of implementation was lower than anticipated by participants and prescriptions were not part of their regular practice. Two themes emerged as factors explaining the gap between their intentions and behaviours: (1) uncertainty about the effectiveness of written PA prescription, and (2) practical concerns (eg, changing well-established habits, time constraints, systemic institutional barriers). CONCLUSION: It may be effective to increase awareness among family physicians about the effectiveness of writing PA prescriptions and address barriers related to how their practice is organised in order to promote written PA prescription rates.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".