Association of Unemployment and Informal Care with Stigma in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) typically affects young adults during their primary productive years. We assessed the magnitude of, and factors associated with, employment status and informal care in people with MS in Canada. METHODS: Data were compiled from the nationally representative cross-sectional Survey on Living with Neurological Conditions in Canada (SLNCC), which included adolescents and adults (age ≥15 years). Employment status was categorized as currently working or not working. The frequency of informal care that people with MS received was categorized as none, less than daily, or daily. Logistic regression analyses were undertaken to identify factors associated with employment status and informal care requirements in people with MS. RESULTS: Of 4409 SLNCC respondents, 631 had MS, of whom 530 were included in the analysis. Of 358 respondents aged 18 to 65 years, 47.8% were not working because of MS; 44.0% reported receiving informal care, with more than half requiring daily care. For caregivers' employment, 15.5% reduced work and 8.2% stopped working because of caregiving. Greater feelings of stigmatization were associated with not working (adjusted odds ratio, 7.42 [95% CI, 2.59-21.28]) and greater informal care (adjusted odds ratio, 3.83 [95% CI, 1.84-7.96]), adjusting for sex, age, education, health-related quality of life, time since MS diagnosis, and comorbidity. CONCLUSIONS: People who feel stigmatized because of their MS are more likely to be unemployed and to require more informal care. Further research is needed to understand the temporal nature of the association between stigma and employment, productivity loss, and informal care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".