Care for capabilities: Implementing the capability approach in rehabilitation of patients with neuromuscular diseases. Study protocol of the controlled before-after ReCap-NMD study
Bibliographic record
Abstract
BACKGROUND: High quality care of patients with neuromuscular diseases requires a personalised approach that focuses on achieving and maintaining a level of functioning that enables them to be in a state of well-being. The capability approach states that well-being should be understood in terms of capabilities, the substantial opportunities that people have to be and do things they have reasons to value. In this Rehabilitation and Capability care for patients with Neuromuscular diseases (ReCap-NMD) study, we want to investigate whether providing care based on the capability approach (capability care) has an added value in the rehabilitation of patients with neuromuscular diseases (NMD). METHODS: Two groups of 30 adult patients with facioscapulohumeral muscular dystrophy or myotonic dystrophy type 1 will be included. The first group will receive rehabilitation care as usual with a follow-up period of 6 months. Then, based on theory, and experiences of patients and healthcare professionals, capability care will be developed. During the following 3 months, the multidisciplinary outpatient rehabilitation care team will be trained in providing this newly developed capability care. Subsequently, the second group will receive capability care, with a follow-up period of 6 months. A mixed methods approach is used with both qualitative and quantitative outcome measures to evaluate the effect of capability care and to perform a process evaluation. The primary outcome measure will be the Canadian Occupational Performance Measure. DISCUSSION: The ReCap-NMD study is the first study to design and implement a healthcare intervention based on the capability approach. The results of this study will expand our knowledge on how the capability approach can be applied in delivering and evaluating healthcare, and will show whether implementing such an intervention leads to a higher well-being for patients with NMD. TRIAL REGISTRATION: Registered at Trialregister.nl (Trial NL8946) on 12th of October, 2020.
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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 | 0.026 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".