Physiotherapy and Low Back Pain in the Injured Worker: An Examination of Current Practice During the Subacute Phase of Healing
Bibliographic record
Abstract
PURPOSE: To describe current physiotherapy practice for injured workers with subacute low back pain (SA-LBP). METHOD: A chart audit of discharged workers was conducted over three episodes of care: 4-6 weeks (T1), 6-8 weeks (T2), and 8-10 weeks (T3) post-injury. The prevalence and reproducibility of parameters for common interventions were calculated as the percentage of active charts over time. Focus groups were used to validate audit results and deepen our understanding of practice. RESULTS: In all, 164 charts were audited. The most prevalent interventions were (1) for manual therapy, joint mobilization and traction; (2) for electrophysical agents (EPAs), heat, ultrasound, and interferential therapy; and (3) for exercise, core stabilization exercises. Transcript analyses revealed that participants viewed injured workers with SA-LBP in a positive light, emphasized the importance of physiotherapy, and discussed SA-LBP in five themes: time frame, non-specific diagnosis, mixed client outlook, change in pain presentation, and the transition from a passive to a more active treatment approach. CONCLUSIONS: The pattern of decreasing passive and increasing active interventions is consistent with the focus-group participants' description of how they approach treatment of clients with SA-LBP. Also noted was a higher prevalence of interventions poorly supported by evidence and lower prevalence of interventions well supported by evidence.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".