Health Status, Stress and Life Satisfaction in a Community Population with MS
Bibliographic record
Abstract
BACKGROUND: Community-based studies can describe health status and related variables in people with Multiple Sclerosis (MS) while avoiding biases introduced by help-seeking in specific clinical settings. OBJECTIVE: To describe general health status, stress perceptions and life satisfaction in people with MS, in comparison to those with other types of disabilities. MATERIALS & METHODS: The Participation and Activity Limitation Survey (PALS) was a post-censual survey conducted by Statistics Canada in association with the 2006 Canadian Census. PALS collected data from a random sample of n = 22,513 respondents identified as having health-related impairments. Frequencies and quartiles as well as mean values, along with associated 95% confidence intervals, were calculated in the analysis. RESULTS: PALS identified 245 individuals with MS. Health status, both perceived and when weighted for societal preference, was markedly lower than that of other disabled groups. No differences in self-perceived stress were seen. People with MS reported lower levels of satisfaction with their health but slightly higher levels of satisfaction with their family and friends. CONCLUSIONS: People with MS report lower levels of general health status and more impairment than those with other disabling conditions. Higher levels of satisfaction with friends and family may reflect psychological adaptation to the 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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".