Exploring physiotherapy practice within hospital-based interprofessional chronic pain clinics in Ontario
Bibliographic record
Abstract
Background: Chronic pain affects one in five persons and is a leading contributor to years lived with disability and high health care costs. In 2016, the government of Ontario increased public funding for pediatric and adult hospital-based interprofessional chronic pain clinics (HICPCs) in Ontario, Canada, expanding the role of physiotherapy in chronic pain management in the province. This role has yet to be described in the literature. Aim: The aim of this study was to explore physiotherapy practice within HICPCs in Ontario. Methods: We conducted an interpretive description qualitative study based on semistructured interviews with physiotherapists employed in pediatric and adult HICPCs in Ontario. Interviews were audio recorded, transcribed verbatim, and reviewed for accuracy. We analyzed interview data using thematic analysis. Results: Ten physiotherapists who practiced in pediatric and adult HICPCs (n = 4 pediatric; n = 6 adult) in Ontario were interviewed between February and April 2020. We constructed five themes related to physiotherapy practice in this setting. Themes included (1) contributing a functional lens to care; (2) empowering through pain education; (3) facilitating participation in physical activity and exercise; (4) supporting engagement in self-management strategies; and (5) implementing a collaborative approach to whole-person care. Conclusions: Our results illuminate how physiotherapy practice within HICPCs in Ontario focuses on providing a collaborative and whole-person approach to care, with an emphasis on supporting patients to increase their functional capacity by promoting engagement in active chronic pain management strategies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".