Perceptions of Physiotherapy Best Practice in Total Knee Arthroplasty in Hospital Outpatient Settings
Bibliographic record
Abstract
PURPOSE: The primary purpose of this study was to examine experienced physiotherapists' perceptions of best practices for patients following total knee arthroplasty (TKA) in publicly funded outpatient hospital settings in the Greater Toronto Area (GTA). The secondary objective was to identify the facilitators of and barriers to implementing best practices in the subacute phase of rehabilitation. METHODS: A qualitative, descriptive, focused ethnographic approach was used to explore physiotherapists' perceptions of best practices for patients with TKA. In-depth semi-structured interviews were conducted with expert physiotherapists acting as key informants. A snowball sampling method was used to recruit physiotherapists in the GTA. Interviews were conducted in person by two of the investigators. RESULTS: Physiotherapists from seven acute-care hospitals in the GTA participated in the study. Analysis of the 140 pages of transcripts from the interviews with 10 physiotherapists revealed that participants perceived best practices as encompassing the adoption of a client-centred approach; inter-professional collaboration; aggressive rehabilitation for patients who are unsuccessful in achieving their outcomes; the use of relevant outcome measures; and consideration of the impact of scarce resources on care. CONCLUSIONS: The findings of this study highlight physiotherapists' perceived best practices for patients with TKA and the unique contribution that hospital-based outpatient physiotherapy can make to patients' rehabilitation.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".