Development and content validation of a questionnaire identifying patients’ functional priorities and abilities after hip or knee arthroplasty
Bibliographic record
Abstract
PURPOSE: To develop a self-report questionnaire evaluating functional priorities after hip or knee arthroplasty and evaluate patients' understanding of its items and conceptual relevance. METHODS: A self-report questionnaire was first developed based on the International Classification of Functioning, Disability, and Health (ICF) core set for osteoarthritis (OA). In the second stage, two research physiotherapists thoroughly reviewed and refined the questionnaire, and another physiotherapist conducted cognitive think-aloud interviews with 18 patients to assess the face and content validity of the questionnaire. RESULTS: All categories and corresponding activities of ICF core set for OA were used to develop the questionnaire. Several questionnaire issues were identified and addressed. Most challenges were related to comprehension, followed by item ordering and visual elements. Patients identified ambiguous wording which we subsequently simplified. Ten activities of the core set were excluded due to lack of face validity, two activities were added, and four activities were modified. CONCLUSION: The findings suggest that the ICF core set for OA needs to be adjusted for patients undergoing hip or knee arthroplasty and highlight the feasibility of applying a modified core set to assess functional priorities after hip or knee arthroplasty.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".