Chiropractors and return-to-work: The experiences of three Canadian focus groups
Bibliographic record
Abstract
OBJECTIVES: To explore the views of chiropractors about timely return-to-work in treating patients with musculoskeletal injuries, to identify the approaches used by chiropractors when treating injured workers with musculoskeletal disorders, and to learn about chiropractors' perspectives on the barriers and facilitators of successful return-to-work. DESIGN: Qualitative study of 3 focus groups of chiropractors. METHODS: Focus groups of 8 to 11 chiropractors were conducted in 3 large Canadian cities. The selected participants were experienced in treating patients with occupational musculoskeletal injuries. Standard questions were used to collect data. The data from each focus group were coded and analyzed separately and then considered in relation to each other. RESULTS: The participants indicated that timely return-to-work depends on patients' characteristics, severity of injury, clinical progress, the availability of work accommodation, and clinical judgment. The chiropractors commented that their treatment of injured workers rests on their strength in diagnosis and treatment and on providing patient-centered care. Positive human relations within workplaces and the ability to accommodate the work of an injured worker were described as important in return-to-work programs. The participants believed that a bias against chiropractic is present within the medical profession and workers' compensation boards. They viewed this bias as an important barrier when assisting their patients to successfully return to work. CONCLUSION: The broad approaches described by the participating chiropractors to return injured workers to work are consistent with those proposed in evidence-based practice guidelines. Better communication among chiropractors, medical doctors, and workers' compensation boards would likely decrease interprofessional tensions and improve the recovery of workers with musculoskeletal injuries.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.035 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".