Different operationalizations of the capability approach in evaluating rehabilitation for persons with neuromuscular diseases: a mixed-methods study
Bibliographic record
Abstract
PURPOSE: The capability approach (CA) is increasingly used in healthcare, but its use in evaluating interventions remains challenging. Therefore, in the Rehabilitation and Capability care for patients with Neuromuscular diseases (ReCap-NMD) study, we conducted a mixed-methods analysis to evaluate how rehabilitation affects capabilities of persons with facioscapulohumeral muscular dystrophy or myotonic dystrophy type 1. We explored whether different operationalizations of the CA yield different results to draw lessons about its use in evaluating rehabilitation. MATERIALS AND METHODS: We compared semi-structured interviews with the ICEpop CAPability measure for Adults (ICECAP-A) and the Canadian Occupational Performance Measure (COPM) in evaluating changes in capabilities of 26 participants during rehabilitation. Based on interviews, participants were categorized as having worsened, unchanged, or improved capabilities. Quantitative analyses (descriptive statistics, Wilcoxon's signed rank tests, Kruskal-Wallis tests) and qualitative comparisons of interview-based categories with ICECAP-A and COPM scores were conducted to identify differences. RESULTS: Participants categorized as having improved capabilities had also higher COPM follow-up scores, while their ICECAP-A scores were unchanged. Changes related to work, energy management, and disease progression anticipation were not captured by the ICECAP-A. CONCLUSIONS: By using a mixed-methods approach, we captured changes in capabilities that occurred during rehabilitation for persons with neuromuscular diseases.
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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 | 0.082 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".