Physical Therapy Health Human Resource Ratios: A Comparative Analysis of the United States and Canada
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Health human resource (HHR) ratios are a measure of workforce supply and are expressed as a ratio of the number of health care practitioners to a subset of the population. Health human resource ratios for physical therapists have been described for Canada but have not been fully described for the United States. In this study, HHR ratios for physical therapists across the United States were estimated in order to conduct a comparative analysis of the United States and Canada. METHODS: National US Census Bureau data were linked to jurisdictional estimates of registered physical therapists to create HHR ratios at 3 time points: 1995, 1999, and 2005. These results then were compared with the results of a similar study conducted by the same authors in Canada. RESULTS: The national HHR ratio across the United States in 1995 was 3.8 per 10,000 people; the ratio increased to 4.3 in 1999 and then to 6.2 in 2005. The aggregated results indicated that HHR ratios across the United States increased by 61.3% between 1995 and 2005. In contrast, the rate of evolution of HHR ratios in Canada was lower, with an estimated growth of 11.6% between 1991 and 2005. Although there were wide variations across jurisdictions, the data indicated that HHR ratios across the United States increased more rapidly than overall population growth in 49 of 51 jurisdictions (96.1%). In contrast, in Canada, the increase in HHR ratios surpassed population growth in only 7 of 10 jurisdictions (70.0%). DISCUSSION AND CONCLUSION: Despite their close proximity, there are differences between the United States and Canada in overall population and HHR ratio growth rates. Possible reasons for these differences and the policy implications of the findings of this study are explored in the context of forecasted growth in demand for health care and rehabilitation services.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".