Occupational Challenges in the Caregivers of People with Multiple Sclerosis: A Qualitative Study
Bibliographic record
Abstract
Background: Multiple Sclerosis (MS) is a neurodegenerative disorder. The progressive and unpredictable nature of MS indicates the patients’ need for long-term care as well as the increased burden of their primary caregivers’ care and occupational challenges that emerge in their daily life activities. Objectives: The current study aimed to explore the occupational challenges caused by engaging in the care process for the caregivers of people with MS Methods: This qualitative study was conducted on 21 caregivers of MS patients using a content analysis approach. Data were collected through semi-structured face-to-face interviews. Results: Three main themes emerged: Time limitations in occupation implementation, care needs in occupation implementation, and emotional reactions affecting occupations. Conclusions: According to the results, the caregivers of people with MS were faced with a variety of occupational challenges. The time limitations for performing routine occupations and desired activities, unmet patient care-facilitating needs, and the occurrence of psychosocial reactions and behaviors were contributed to these challenges and their exacerbation. Identifying these challenges is both useful for designing interventional programs and to help caregivers to successfully perform their desired occupations in spite of challenges in the care process.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".