Impact of severity of multiple sclerosis on caregivers’ occupational performance and coping strategies
Bibliographic record
Abstract
Background: Due to the unpredictable and progressive nature of multiple sclerosis, the impact of MS is felt beyond the diagnosed individual, extending to the caregivers supporting them. This study aimed to explore how multiple sclerosis severity affects caregivers' occupational performance outcomes, including the role of coping strategies in these outcomes. Methods: = 32), caregivers of patients with severe multiple sclerosis severity. Caregivers were assessed using a sociodemographic information form, the Canadian Occupational Performance Measure, and the Coping with Multiple Sclerosis Caregiving Inventory. Findings: A total of 282 participants, including 141 multiple sclerosis patients and 141 caregivers, were enrolled. Caregivers reported low-to-average occupational performance, with the greatest challenges in self-care, leisure, and productivity. Significant inverse correlations were found between multiple sclerosis severity and both occupational performance and caregiver satisfaction. Caregivers of patients with severe multiple sclerosis faced more difficulties, particularly in Avoidance, Practical Assistance, and Satisfaction. Multiple regression showed that caregiver satisfaction was influenced by age, caregiving duration, and patient gender. Conclusion: This study revealed the significant impact of multiple sclerosis severity on caregivers' occupational performance and coping strategies, highlighting the need for effective interventions such as caregiver training programs, psychological support, and respite care, especially for those with advanced multiple sclerosis. Policymakers must focus on creating robust caregiver support systems to alleviate the socioeconomic impact of multiple sclerosis on families and communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".