Community Engagement Integral to Reimagining Rural and Remote Physiotherapy in Saskatchewan: Findings from a World Café Multi-Stakeholder Event
Bibliographic record
Abstract
Purpose: To provide recommendations to improve access to physiotherapy services in underserved rural, remote, and Indigenous communities across Saskatchewan. Method: Through a World Café–facilitated discussion, 55 multi-stakeholder participants (including facilitators) discussed ways to improve access to physiotherapy services in rural, remote, and Indigenous communities in Saskatchewan. Qualitative description happened through qualitive content analysis from data captured during the World Café–facilitated discussion. Results: Community engagement was found to be a central code, along with the interrelated categories: training programme and student experience; recruitment of physiotherapists; retention and physiotherapy practice; community- and team-based models of care; and virtual care and technology. Conclusions: Recommendations to improve access to physiotherapy services in Saskatchewan spanned from targeted recruitment and training of physiotherapy students to supporting physiotherapists who practise in geographically isolated environments. Community engagement was highlighted as necessary to ensure effective, sustainable, and appropriate measures to better train, recruit, retain, and support physiotherapists and access to physiotherapy services in rural and remote areas across Saskatchewan.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".