Models of Integrating Physical Therapists into Family Health Teams in Ontario, Canada: Challenges and Opportunities
Bibliographic record
Abstract
PURPOSE: To explore the potential for different models of incorporating physical therapy (PT) services within the emerging network of family health teams (FHTs) in Ontario and to identify challenges and opportunities of each model. METHODS: A two-phase mixed-methods qualitative descriptive approach was used. First, FHTs were mapped in relation to existing community-based PT practices. Second, semi-structured key-informant interviews were conducted with representatives from urban and rural FHTs and from a variety of community-based PT practices. Interviews were digitally recorded, transcribed verbatim, and analyzed using a categorizing/editing approach. RESULTS: Most participants agreed that the ideal model involves embedding physical therapists directly into FHTs; in some situations, however, partnering with an existing external PT provider may be more feasible and sustainable. Access and funding remain the key issues, regardless of the model adopted. CONCLUSION: Although there are differences across the urban/rural divide, there exist opportunities to enhance and optimize existing delivery models so as to improve client access and address emerging demand for community-based PT 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".