“It ... doesn’t always make it [to] the top of the list”: Primary care physicians’ experiences with prescribing exercise for knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To explore primary care physicians’ (PCPs’) experience with and barriers to prescribing exercise for people with knee osteoarthritis (OA). DESIGN: A qualitative descriptive study using semistructured interviews. SETTING: Ontario. PARTICIPANTS: Twelve PCPs recruited from academic and community family health practices. METHODS: Twelve 30- to 60-minute, one-on-one interviews were conducted using a purposive sampling of PCPs. Data were analyzed using a constant comparison approach. MAIN FINDINGS: Of the 12 interviews, 11 were analyzed and organized in relation to the primary finding that PCPs often assigned a low priority both to OA as a disease and to exercise as a treatment. It was discovered that exercise, the main treatment for OA, is often not perceived as a “real” medical treatment; prescribing exercise is perceived as being outside of most PCPs’ scope of practice; and PCPs often account for success or failure of prescribed exercise as being the function of individual patient motivation. CONCLUSION: Although knee OA often affects incidence of and complicates other comorbidities, in general, PCPs consider knee OA to be lower in importance relative to other diseases they manage. Improved awareness of OA and its effect on other chronic conditions might improve uptake of OA treatment, including exercise. If additional guidance on exercise is needed, referring patients to a physiotherapist is a potential solution.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".