Care Partners and Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Caring for someone with multiple sclerosis (MS) can be a stressful experience that requires clinical attention. We investigated the impact of caregiver stress on the emotional well-being and physical health of the MS care partner using the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry. METHODS: Care partners of NARCOMS participants were invited to complete an online questionnaire that captured demographic characteristics, health status, caregiver burden as measured by the Zarit Caregiver Burden Interview, and impact of caregiving on employment. RESULTS: Of 1446 care partners who agreed to participate, 1333 had complete data. Most were men (n = 825, 61.9%), with a mean (SD) age of 51.1 (11.2) years. The mean (SD) Zarit total score was 24.6 (15.1), placing the overall group in the mild caregiver burden range. Compared with male care partners, female care partners reported higher levels of burden and stress and more medication use for stress/anxiety and mood disorders. Male care partners were more likely to report physical concerns. Care partners of people with primary progressive MS reported greater perceived burden than did partners of people with secondary progressive MS and relapsing-remitting MS. More than 40% of care partners (559 of 1288) had missed work during the past year owing to caregiving responsibilities. CONCLUSIONS: Care partners of people with MS have substantial physical and psychological health concerns and experience an adverse impact on employment. Future research should evaluate how to mitigate the adverse effects of caregiving and evaluate positive aspects of the role.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.001 |
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".