Factors Associated with Postrelapse Rehabilitation Use in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Most people with multiple sclerosis (MS) have periodic and unpredictable relapses as part of their disease course. Relapses often affect functional abilities, resulting in diminished productivity and lower quality of life. Considering the effects, rehabilitation can play an important role in facilitating recovery; yet, the current literature suggests a lack of postrelapse rehabilitation services use. This study aims to document postrelapse rehabilitation services use and estimate the extent to which predisposing characteristics, perceived need, and enabling resources were associated with postrelapse rehabilitation services use in adults with MS. METHODS: This cross-sectional study used convenience sampling, and data from 73 adults with MS who recently had a relapse in the United States and Canada were analyzed. RESULTS: A total of 25 participants (34.2%) reported using postrelapse rehabilitation services. The regression model identified three variables associated with postrelapse rehabilitation services use: age (odds ratio [OR], 1.075), self-reported quality of life (considerably affected by the most recent relapse [OR, 5.717]), and presence of helpful health care providers (for obtaining postrelapse rehabilitation services [OR, 5.382]). CONCLUSIONS: Most participants experienced a range of symptoms or limitations because of their most recent relapse, affecting their daily activity and quality of life. However, only one-third of the participants reported using postrelapse rehabilitation services, which focused on the improvement of their physical health. Regression modeling revealed that three population characteristics of the Andersen Behavioral Model of Health Services Utilization were associated with postrelapse rehabilitation services use.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".