Mapping Physiotherapy Use in Canada in Relation to Physiotherapist Distribution
Bibliographic record
Abstract
Purpose: In this cross-sectional study, we examined the distribution of physiotherapists at the health region level across Canada in relation to self-reported physiotherapy use across the provinces and territories. Method: We drew on two data sources: the physiotherapy use question from the 2014 Canadian Community Health Survey and physiotherapists’ primary employment information, obtained from the Canadian Institute of Health Information’s 2015 Physiotherapist Database. We then applied geospatial mapping and Pearson’s correlation analysis to the resulting variables. Results: Physiotherapy use is moderately associated with the distribution of physiotherapists (Pearson’s r92 = 0.581, p < 0.001). The use and distribution variables were converted into three categories using SDs of 0.5 from national means as cut-off values. Cross-classification between the variables revealed that 15.2% of health regions have a high use–high distribution ratio; 18.5% have a low use–low distribution ratio; 4.3% have a high use–low distribution ratio; 2.2% have a low use–high distribution ratio; and 60.0% have medium use–medium distribution ratio. Conclusions: The distribution of physiotherapists and self-reported physiotherapy use varies across health regions, indicating a potential inequality in geographical access. Given that most provinces have a regionalized approach to health human resources and health service delivery, these findings may be helpful to managers and policy-makers and may allow them to make a more granular comparison of intra- and inter-provincial differences and potential gaps.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".