Physical Therapy Management of Low Back Pain: A Survey of Physiotherapists’ Current Assessment and Treatment Practices
Bibliographic record
Abstract
Purpose: The purpose of this study was to determine current physiotherapy practice for managing chronic low back pain (LBP). Method: We administered a cross-sectional survey to all physiotherapists working in Eastern Health (EH) Regional Health Authority, Newfoundland and Labrador, by email. To ascertain how physiotherapists assessed and treated patients with LBP, the survey included multiple-choice and open-ended questions, along with case vignettes. We explored the respondents’ confidence about implementing all aspects of guideline-based care, as well as their use of treatment outcome measures. Results: A total of 76 physiotherapists responded to the survey (84% response rate); 56 (74%) reported that they treated patients with LBP as part of their regular practice. More than half had managed LBP for more than 10 years. The most frequently used treatments were self-management advice, followed by home and supervised exercise. The majority of respondents lacked confidence about implementing cognitive–behavioural treatment techniques. The Numeric Pain Rating Scale was the most commonly used outcome measure; disability outcome measures were not frequently used. Conclusions: The majority of LBP management in EH aligns with guideline recommendations. Increased uptake of guidelines recommending assessment and management of LBP using a bio-psychosocial approach will require training and support.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".