Practice patterns of physiotherapists in the treatment of work‐related back pain
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: Although physiotherapists (PTs) are one of the health professionals most involved in the treatment of back pain, their practice patterns have not been well studied. The study objectives were to identify the practice patterns of PTs treating workers suffering from acute/subacute back pain, with and without radiating pain below the knee, and to assess the relationship between these patterns and characteristics of PTs. METHODS: PTs working in private clinics in the province of Quebec, Canada were invited to participate. Each PT used a self-administered questionnaire to record, for each treatment session, treatment objectives, interventions used and education given to two workers with back pain, one without radiating pain (n = 189 PTs) and one with radiating pain (n = 136 PTs). Multiple correspondence analysis with hierarchical classification was used to identify practice patterns of PTs. Multinomial logistic regressions were used to assess the relationship between practice patterns and PTs characteristics. RESULTS: For workers without radiating pain, 51.9% of PTs focused their treatment on soft tissue mobilizations/massage and heat, 24.3% focused on the McKenzie approach and related interventions, and 23.8% focused on exercises and function. Similar results were found for workers with radiating pain. Most of the PT characteristics were not related to practice patterns. CONCLUSIONS: The practices of PTs appeared to be separated into three distinct patterns. These practice variations suggest that there may be disagreement or uncertainty among PTs in the management of work-related back pain. The lack of evidence for the majority of interventions used by PTs and the difficulties of integrating evidence into clinical practice may be possible explanations.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".