Evidence-Based Practice for Non-Specific Low Back Pain: Canadian Physiotherapists’ Adherence, Beliefs, and Perspectives
Bibliographic record
Abstract
Purpose: Physiotherapists are key providers of care for patients with low back pain (LBP); however, information on Canadian physiotherapists’ use of evidence-based clinical practice guidelines (EBCPGs) for LBP is lacking. We aimed to (1) describe Canadian physiotherapists’ adherence to EBCPGs for LBP; (2) compare beliefs and attitudes of physiotherapists with higher and lower adherence; (3) identify predictors of adherence; and (4) gather physiotherapists’ perceptions about the care provided to patients with LBP. Method: This mixed methods study involved two phases: (1) a survey containing a LBP clinical scenario and (2) qualitative semi-structured interviews with physiotherapists. Results: A total of 406 (77%) of the 525 survey respondents demonstrated higher adherence (score of 3 or 4) to EBCPGs; however, only 29.5% chose interventions to address psychosocial issues. Postgraduate training was the strongest predictor of higher adherence. Interviewed physiotherapists reported being highly satisfied with the care provided to patients with LBP even when psychosocial issues are present, despite low confidence in addressing those issues. Conclusions: Although overall adherence was high, Canadian physiotherapists do not frequently address psychosocial issues with LBP patients, and often do not feel confident or competent in that aspect of practice. This suggests an opportunity for developing additional training for addressing psychosocial issues in LBP patients.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".