Physiotherapy Models of Service Delivery, Staffing, and Caseloads: A Profile of Level I Trauma Centres across Canada
Bibliographic record
Abstract
PURPOSE: To examine and describe physiotherapy models of service delivery, staffing, and caseloads in Level I trauma centres across Canada. METHODS: A telephone questionnaire was administered to one experienced trauma physiotherapist at each of the 19 Level I trauma centres in Canada. Quantitative data were analyzed descriptively for national trends. RESULTS: Data were collected from all 19 centres (100%), 89% of which provided physiotherapy services 5 days per week with priority weekend coverage. Physiotherapist assistants (PTAs) were employed by 89% of centres and were used across the continuum of care. Centres with PTAs appear to be more likely to provide patients with additional daily treatment. Departmental organizational structures were the most common (41%) and were associated with higher caseloads. Higher caseloads also appear to be linked with having less than 10 years of experience as a physiotherapist. CONCLUSIONS: Variations exist between centres with respect to the delivery of physiotherapy services. These variations may result from differences in province-specific legislation, differences in funding structure, and the lack of evidence-informed guidelines. Future research is needed to establish optimal models of physiotherapy services that are cost-effective and provide best patient care.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".