A Qualitative Study on Prehabilitation before Total Hip and Knee Arthroplasties: Integration of Patients’ and Clinicians’ Perspectives
Bibliographic record
Abstract
To explore and integrate the perspectives of patients with hip and knee osteoarthritis (OA), their caregivers, and clinicians who are working with these patients about current preoperative rehabilitation (“prehab”) content and delivery. Participants were individuals with hip (n = 46) or knee OA (n = 14), their family caregivers (n = 16), and clinicians working with patients with hip/knee OA (n = 15). In semi-structured interviews and focus groups, participants answered questions regarding barriers to accessing prehab, gaps in prehab content, learning preferences, and delivery formats. Interviews were audiotaped and transcribed verbatim. Data were analyzed using Qualitative Description method. Four main themes were identified: (1) “I didn’t get any of that” discusses barriers in accessing prehab; (2) “I never got a definitive answer” highlights necessary information in prehab; (3) “better idea of what’s going to happen” emphasizes the positive and negative aspects of prehab; (4) “a lot of people are shifting to online” describes participants’ perspectives on online education. Our findings confirm the need for prehab education and the potential of online prehab education. The results inform the development of prehab educational modules based on users’ input.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".