The Distribution of Physiotherapists in Ontario: Understanding the Market Drivers
Bibliographic record
Abstract
PURPOSE: To understand the factors that affect the distribution of physiotherapists in Ontario by examining three potential influences in the multi-payer physiotherapy (PT) market: population need, critical mass (related to academic health science centres [AHSCs]), and market forces. METHODS: Physiotherapist density and distribution were calculated from 2003 and 2005 College of Physiotherapists of Ontario registration data. Physiotherapists' workplaces were classified as not-for-profit (NFP) hospitals, other NFP, or for-profit (FP), and their locations were classified by census division (CD) types (cities and counties). RESULTS: Physiotherapist density varied significantly and distribution was neither uniformly responsive to population need, nor driven primarily by market forces. The largest factor was an AHSC in a CD; physiotherapists locate disproportionately in NFP hospitals in AHSCs rather than in the growing FP sector. CONCLUSIONS: While some patterns can be discerned in the distribution and densities of physiotherapists across Ontario, further work needs to be done to identify why population need and market forces appear to be less influential, and why CDs with AHSCs are so attractive to physiotherapists. With this additional information, it may be possible to identify ways to influence uneven distribution in the future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".