Occupational performance of individuals with Multiple Sclerosis based on disability level in Iran
Bibliographic record
Abstract
Background: Multiple sclerosis (MS) is a common disease across the world as well as in Iran. Individuals with MS usually experience occupational performance problems that result in limitations in their daily life. This study aimed to determine the occupational performance of individuals with MS based on the disability level in Iran. Methods: In this cross-sectional study, 50 individuals with MS (20 to 50 years old) were recruited through a convenience sampling strategy from different clinics in Arak City, Iran, during 2016-2017. The Persian versions of Canadian Occupational Performance Measure (COPM) and Expanded Disability Status Scale (EDSS) were used to assess the status of occupational performance and level of disability. The data were analyzed using chi-square, Spearman's rank correlation, and Mann-Whitney U tests. Results: The total number of 248 occupations were identified as difficult to perform in the following areas: 125 (50.40%) in self-care, 58 (23.38%) in productivity, and 65 (26.20%) in leisure. In addition, the prioritized occupations (n = 149, median: 3, range: 1-4) had significant difference in the distribution of occupations compared with the non-prioritized occupations (P < 0.0001) and the ratings for performances and satisfactions were generally low. There were significant differences between the occupational performance and level of EDSS. Conclusion: The findings of current study suggest that individuals with MS suffer from widespread problems in the areas of occupational performance, particularly in self-care. The findings emphasize the need for identifying the problems of daily occupations in individuals with MS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".