A Snapshot of Hip and Knee Replacement Rehabilitation Care Across Canada: A Cross-Sectional Survey Using an Online Clinician Quality Indicator Questionnaire
Bibliographic record
Abstract
Purpose: To determine usability, feasibility, and reliability of an online questionnaire to assess clinicians’ adherence to 10 postacute rehabilitation quality indicators (QIs) for total hip (THR) and knee replacement (TKR) and explore current rehabilitation practices across Canada. Method: Following usability testing with clinicians in BC and Quebec, we recruited rehabilitation clinicians across Canada to complete the online survey. Respondents rated adherence (past 3 months), importance, and feasibility for 10 QIs. We resent the survey 2–3 weeks later (test-retest reliability). Results: Based on usability testing, we made minor changes in wording, altered response options, and created a French language version. In total, 238 clinicians completed all or parts of the English ( n = 123) and French ( n = 115) questionnaires. Respondents mostly practised in the public sector (88%) and outpatient settings (42%). On average, clinicians met (“always” or “often” response) 23.3% (SD 13.7%, 95% CI: 21.1, 25.4) of THR and 25.5% (SD 15.1%, 95% CI: 23.0, 27.9) of TKR indicators. There were mixed views on the importance and feasibility of the QIs. Varied rehabilitation formats, duration, and dosage were described. Conclusions: Canadian rehabilitation clinicians report low overall adherence to THR and TKR rehabilitation QIs and differing rehabilitation approaches and models of care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".