Understanding Physiotherapists' Roles in Ontario Primary Health Care Teams
Bibliographic record
Abstract
PURPOSE: To understand physiotherapists' roles and how they are enacted within Ontario primary health care (PHC) teams. METHODS: Following a pragmatic grounded theory approach, 12 physiotherapists practising within Ontario PHC teams participated in 18 semi-structured in-depth in-person interviews. All interviews were audiotaped and transcribed verbatim, then entered into NVIVO-8. Coding followed three progressive analytic stages and was iterative in nature, guided by grounded theory. An explanatory scheme was developed. RESULTS: Physiotherapists negotiate their place within the PHC teams through five interrelated roles: (1) manager; (2) evaluator; (3) collaborator; (4) educator; and (5) advocate. These five roles are influenced by three contextual layers: (1) inter-professional team; (2) community and population served; and (3) organizational structure and funding. Canada's PHC mandate (access, teams, information, and healthy living) frame the contexts that influence role enactment. CONCLUSIONS: To fulfill the PHC mandate, physiotherapists carry out multiple roles that are based on a broad holistic perspective of health, within the context of a collaborative inter-professional team and the community, through an evidenced-informed approach to care. There appear to be multiple ways of successfully integrating physiotherapists within PHC teams, provided that role enactment is context sensitive and congruent with the mandate of PHC.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 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".