Gaps in Medicare and the Social Safety Net Predict Financial Strain Among Older Canadians With Multiple Sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) can create significant financial burden, with cost of living rising consistently with increasing age and disability. We aimed to determine the prevalence and predictors of financial strain among a large sample of older Canadians with MS. A binomial logistic regression, which estimates the probability of an event happening (financial strain—yes/no), was performed. Participants were 64.6 ( SD ± 6.2) years old and reported living with MS symptoms 32.8 ( SD ± 9.4) years. In total, 22% of participants experienced financial strain. Predictors of financial strain (from greatest to least) were not having private health insurance, job loss due to MS, having moderate to high stress, greater physical impact of MS, not having home adaptations, not having social support, and living alone. These findings point to insufficiencies in Canada’s health and social systems when it comes to the provision of universal care to those living with disabling neurological chronic illness.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".