Acceptability of physiotherapists as primary care practitioners for the care of people with musculoskeletal disorders: a French population-based cross-sectional survey
Bibliographic record
Abstract
OBJECTIVES: In France, early access to physiotherapy for people with musculoskeletal disorders (MSKDs) depends on prescription and referral by the family physician in the physician-led model of care. The readiness of French people for direct access to physiotherapy is not known. This survey aims to identify the perceptions of French adults regarding physiotherapists' competence to diagnose and manage MSKDs if they were primary care practitioners, confidence in their ability to provide quality care, and satisfaction with the last episode of care for those concerned; and to identify factors associated with these three variables. DESIGN: French population-based cross-sectional survey. PARTICIPANTS: A representative sample of the French adult population was surveyed between June 2020 and September 2021. OUTCOME MEASURES: Collected variables included previous physiotherapy experience, perception of competence to diagnose and manage MSKDs as primary care practitioners, confidence about quality of care, and self-referral preferences. Multivariate logistic regression analyses were performed to identify the factors associated with these three variables. RESULTS: A total of 1000 participants completed the survey; 854 (85%) believed that physiotherapists would be competent primary care practitioners, and 920 (92%) were confident about the quality of care. Most had previously consulted a physiotherapist (n = 823, 82%); of these, 762 (91%) were satisfied with care received. CONCLUSION: This large sample of French adults considered physiotherapists as competent to diagnose and treat some MSKDs as primary care practitioners, and that they provided quality care. Further studies should investigate the scope of care, safety, and efficacy of a direct access physiotherapy model. CONTRIBUTION OF PAPER.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".