Facilitators and Barriers to Integrating Sleep Recommendations for Counselling Patients With Arthritis: A Survey of Physiotherapists and Students in Canada
Bibliographic record
Abstract
Purpose: Individuals with arthritis face challenges in balancing activity, rest, and sleep. While physiotherapists address activity, sleep considerations are often overlooked, despite evidence suggesting better sleep improves treatment outcomes. The purpose of this study was to describe facilitators and barriers for physiotherapists and students incorporating sleep in practice. Method: We conducted a self-administered electronic survey among physiotherapists and students in Canada. The survey included 28 items (7-point Likert scale) mapped on the Capability-Opportunity-Motivation-Behaviour system and Theoretical Domains Framework. We report means (95% CIs) and identified facilitators (mean >5/7) and barriers (mean <4/7) by item. Results: Between January and November 2023, 216 responded and 191 (88%) completed the survey (149 physiotherapists; 42 students). Mean age was 35 (SD 11) years. Most delivered in-person care (67%) in urban settings (67%). Facilitators (mean >5/7) included the belief that sleep health is within practice scope, optimism about its benefits, awareness of sleep's importance in managing arthritis, learning about movement guidelines, and plans to integrate sleep education into arthritis treatment. Barriers (mean <4/7) included being unaware of where to find sleep resources (mean 3.63 [95% CI: 3.39, 3.89]) and lacking knowledge about sleep guidelines (mean 3.78 [95% CI: 3.46, 4.10]) and lack of confidence in guiding sleep for patients (mean 3.38 [95% CI: 3.10, 3.69]). Few reported providing sleep education for patients with arthritis (mean 3.52 [95% CI: 3.27, 3.77]), or observing similar behaviours from colleagues (mean 3.60 [95% CI: 3.40, 3.80]). Conclusions: While physiotherapists and students show positive perceptions about sleep health in practice, challenges remain for effective implementation. These findings can inform the development of theory-informed behaviour-change interventions to engage physiotherapists in greater sleep integration in practice.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".