Physiotherapists and Physiotherapy Student Placements across Regions in Ontario: A Descriptive Comparison
Bibliographic record
Abstract
PURPOSE: To describe the distribution and type of physiotherapy student placements in one year relative to the number of practising physiotherapists of Ontario. METHODS: Site information about physiotherapy students' clinical placements in Ontario in 2010 was obtained from Academic Coordinators of Clinical Education. Worksite information about physiotherapists who reported providing direct patient care at a primary employment site in Ontario and at least 600 practice hours in their annual renewal was obtained from the College of Physiotherapists of Ontario. Each placement and each physiotherapist was attributed to one of Ontario's 14 local health integration networks (LHINs). For each LHIN, a ratio of student placements to practising physiotherapists was calculated, using summed counts. Counts of placement types by setting, patient mix, and practice area were also calculated for each LHIN. RESULTS: The 5 LHINs in which the university programmes are located had high placement:physiotherapist ratios, from 0.92 to 0.38. The other 9 LHINs had lower ratios, the 3 lowest at approximately 0.15. There was a wide mix of clinical placement types across LHINs. CONCLUSION: Physiotherapists' participation in physiotherapy students' clinical education varied widely among Ontario regions. Future research could explore whether regional differences are persistent, why they occur, and whether they should be reduced.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".