Mapping the Physiotherapy Profession in Saskatchewan: Examining Rural versus Urban Practice Patterns
Bibliographic record
Abstract
PURPOSE: People living in rural and remote regions need support to overcome difficulties in accessing health care. The objectives of the study were (1) to compare demographic characteristics, professional engagement indicators, and clinical characteristics between physiotherapists practising in rural settings and those practising in urban settings and (2) to map the distribution of physiotherapists in Saskatchewan. METHOD: This cross-sectional study used de-identified data collected from the 2013 Saskatchewan College of Physical Therapists membership renewal (n=643), linked with the Saskatchewan Physiotherapy Association's (SPA) 2012 membership list and a list of physiotherapists who had served as clinical instructors. Employment location (rural vs. urban) was determined by postal code. RESULTS: Only 11.2% of Saskatchewan physiotherapists listed a rural primary employment location, and a higher density of physiotherapists per 10,000 people work in health regions with large urban centres. Compared with urban physiotherapists, rural physiotherapists are more likely to provide direct patient care, to provide care to people of all ages, and to have a mixed client level, and they are less likely to be SPA members. CONCLUSIONS: Rural and urban physiotherapists in Saskatchewan have different practice and professional characteristics. This information may have implications for health human resource recruitment and retention policies as well as advocacy for equitable access to physiotherapy care in rural and remote regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".