Examining the Supply of and Demand for Physiotherapy in Saskatchewan: The Relationship between Where Physiotherapists Work and Population Health Need
Bibliographic record
Abstract
Purpose: This research examined the association between the distribution of physiotherapists in Saskatchewan relative to population health characteristics and self-reported physiotherapy use. Methods: Using a cross-sectional design, de-identified data were collected from the 2013 Saskatchewan College of Physical Therapy membership renewals (n=643), and Saskatchewan population health characteristics data were obtained from the 2009–2012 Canadian Community Health Surveys (CCHSs). Age- and sex-adjusted proportions of selected population health characteristics were calculated and stratified by health region and rural–urban location; both were determined, for physiotherapists and CCHS participants, using postal codes. The association between physiotherapy distribution and physiotherapy use was calculated, and geospatial mapping techniques were used to display physiotherapist distribution across the province relative to population health characteristics. Results: Across health regions, a positive correlation (r=0.655, p<0.029) was found between physiotherapist distribution and self-reported physiotherapy use. Mapping population health characteristics according to physiotherapist distribution demonstrated an imbalance between supply and distribution of physiotherapists and population health needs and demands. Conclusion: There is a discrepancy in Saskatchewan among the distribution of physiotherapists, self-reported physiotherapy use, and population health characteristics, especially in rural settings. These findings provide insight into which areas are in need of increased physiotherapy services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".