How should family physicians provide physical activity advice? Qualitative study to inform the design of an e-health intervention.
Bibliographic record
Abstract
OBJECTIVE: To explore patient attitudes toward interacting with family physicians regarding physical activity in order to inform the development of an e-health intervention aimed at helping family physicians support patients in becoming more physically active. DESIGN: Qualitative study. SETTING: Women's College Hospital in Toronto, Ont. PARTICIPANTS: Ten patients recruited from the academic family practice health centre. METHODS: Semistructured interviews were conducted with patients using maximum variation sampling until thematic saturation was reached. Interviews explored past experiences and preferences for receiving physical activity advice from family physicians, and tools or techniques that might support increasing physical activity. Interviews were audiorecorded, transcribed, and coded independently by members of the research team before undergoing thematic analysis. MAIN FINDINGS: and recommending tools that incorporate planning, goal-setting, and goal-monitoring features. CONCLUSION: Ultimately, physical activity recommendations from family physicians cannot make a difference if patients do not act on them. This study elicits input from patients to develop preliminary strategies that might help family physicians provide physical activity advice in a more patient-centred fashion. Further research is needed to test interventions that help implement these strategies and to assess their effect.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".