Geographic Accessibility to Pelvic Health Physiotherapy Services Across Ontario: A Geographic Information System Analysis
Bibliographic record
Abstract
Purpose: The purpose of this study was to explore the distribution and geographic accessibility of pelvic health physiotherapist services for females in Ontario across an urban-rural gradient. Method: A geographic information system-based observational study was conducted. Physiotherapists’ practice locations (supply) were obtained from the College of Physiotherapists of Ontario registrant pelvic health roster. Dissemination area centroids (demand) were calculated based on 2021 Statistics Canada census data for females in Ontario. Locations were geocoded and mapped, and an accessibility score was computed using an enhanced two-step floating catchment area method. A choropleth map was generated with accessibility scores, and results were stratified using an urban-rural gradient. Results: Geographic locations of 1,172 pelvic health physiotherapists across Ontario were retrieved. The provincial average accessibility score was 1.84 pelvic health physiotherapists (PHPTs) per 10,000 females (range, min-max, 0–26.27 PHPTs). Access to a PHPT is more limited in rural areas and municipalities with weak to no metropolitan influence and higher in census metropolitan areas and larger urban centres. Conclusions: Regional disparities in accessibility to PHPT services for females in Ontario exist and should be considered in planning and policy development, particularly to enhance accessibility to females living in rural areas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".