Management of Low Back Pain by Physical Therapists in Quebec: How Are We Doing?
Bibliographic record
Abstract
PURPOSE: In this study, we characterized physiotherapists' attitudes and beliefs about the bio-psychosocial problem of low back pain (LBP), their use of clinical practice guidelines (CPGs), and the extent to which their advice and treatment is in line with best-evidence CPGs. METHODS: One hundred eight physiotherapists completed an online survey that included questionnaires exploring the strength of physiotherapists' biomedical and bio-psychosocial orientations toward the management of LBP: the Pain Attitudes and Beliefs Scale for Physiotherapists and the Attitudes to Back Pain Scale for musculoskeletal practitioners. In addition, participants responded to questions about treatment recommendations for patients in two vignettes. RESULTS: Only 12% of respondents were aware of CPGs. Physiotherapists with a stronger biomedical orientation scored the severity of spinal pathology higher in the patient vignettes. A stronger biomedical orientation was also associated with disagreement with recommendations to return to usual activity or work. CONCLUSIONS: The results suggest limited awareness by physiotherapists of best-evidence CPGs and contemporary understandings of LBP that support early activation and self-management. Research to better understand and facilitate the implementation of best-evidence professional education and clinical practice is an urgent priority.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".