Understanding the Health and Well-being of Women With Multiple Sclerosis
Bibliographic record
Abstract
ABSTRACT: BACKGROUND: Multiple sclerosis (MS) is an immune-mediated disease that affects the central nervous system, and is potentially disabling. Women experience MS more frequently than men at a 3:1 ratio. Current literature suggests that women may experience health, social determinants of health, and disability differentially, and there is a gap in the research examining how gender intersects with MS. METHODS: Interviews were conducted with 23 women with MS. van Manen's hermeneutic phenomenology was used to inform and analyze the data to understand the nature and meaning of health and well-being for participants. RESULTS: A key theme of "enhancing wholeness for women with MS" emerged from the data, which suggests that women with MS view themselves as healthy and "whole" despite living with MS. Supporting factors for physical, mental, and social well-being include the ability to enact human agency within social structures such as with employment or seeking care with MS clinics. The findings informed the development of a figure that depicts the supporting factors of health and well-being for women living with MS. CONCLUSION: The health and well-being of women with MS may be optimally supported by nurses and interdisciplinary healthcare teams through careful consideration as to how agency is enacted within social structures, for example, MS clinics, employment, and social support systems, as well as considerations for social determinants of health.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".