Making Decisions about Service Provision for Clients with Low Back Pain: Perspectives of Canadian Physiotherapy Professionals
Bibliographic record
Abstract
Purpose: This study identified the individuals responsible for making decisions about physiotherapy (PT) wait time, frequency of treatment, and treatment duration for persons with low back pain and determined which factors guided these decisions. Method: A cross-sectional survey was sent to Canadian PT professionals treating adult patients with musculoskeletal problems. It included a clinical vignette describing a patient with low back pain. Respondents were asked who made decisions about wait time, treatment frequency, and treatment duration as well as on which factors they based these decisions. Results: Clinicians were most often responsible for making decisions about treatment frequency and duration. Although clinicians and managers or coordinators were mainly responsible for making decisions about wait time, there was more variability depending on sector of care: in the private sector, administrative assistants played a much larger role. Clinical judgment, clinical guidelines, and patients’ demands were the predominant factors influencing wait time and frequency decisions. Treatment duration was related to patients’ goals, clinical progression, patients’ motivation, and patients’ return to work. Conclusions: Decisions about service provision for wait times are made by a range of stakeholders, and a wide variety of factors guide Canadian PT professionals’ decision making. Identifying these factors is essential for informing a discussion of decisions about evidence-based and equitable service delivery so that the actors involved can reach a consensus.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".