Healthy Aging from the Perspectives of 683 Older People with Multiple Sclerosis
Bibliographic record
Abstract
Purpose. The aim of this study was to determine what factors most greatly contributed to healthy aging with multiple sclerosis (MS) from the perspective of a large sample of older people with MS. Design and Methods. Participants (n = 683; >55 years of age with symptoms >20 years) provided answers to an open-ended question regarding healthy aging and were categorized into three groups, 55-64 (young), 65-74 (middle), and 75 and over (oldest old). Sociodemographic actors were compared using ANOVA. Two independent raters used the framework method of analyzing qualitative data. Results. Participants averaged 64 years of age (±6.2) with MS symptoms for 32.9 years (±9.4). 531 participants were female (78%). The majority of participants lived in their own home (n = 657) with a spouse or partner (n = 483). Participants described seven themes: social connections, attitude and outlook on life, lifestyle choices and habits, health care system, spirituality and religion, independence, and finances. These themes had two shared characteristics, multidimensionality and interdependence. Implications. Learning from the experiences of older adults with MS can help young and middle aged people with MS plan to age in their own homes and communities. Our data suggests that older people with MS prioritize factors that are modifiable through targeted self-management strategies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".