Access to Care and Health-Related Quality of Life in Multiple Sclerosis
Bibliographic record
Abstract
Background and Objectives: Despite their high health care use, it is unclear whether the health care needs of people with MS are being met and what their priorities are. We assessed priorities for access to, and affordability of care, by people living with MS in the United States. We also tested the association between perceived inadequate access to care and health-related quality of life (HRQoL). Methods: In Fall 2022, we conducted a cross-sectional survey of participants in the North American Research Committee on Multiple Sclerosis Registry about access to care and HRQoL (Health Utilities Index Mark III). We used multivariable polytomous logistic regression to test sociodemographic and clinical factors associated with access to care. We used multivariable linear regression analysis to test the association between access to care and HRQoL. Results: We included 4,914 respondents in the analysis, of whom 3,974 (80.9%) were women, with a mean (SD) age 64.4 (9.9) years. The providers who were most reported as needed but inaccessible were complementary providers (35.5%), followed by allied health providers (24.2%), occupational therapists (22.7%), and mental health providers (20.7%). Over 80% of participants reported that it was important or very important to be able to get an appointment with their primary MS health care provider when needed, to have sufficient time in their appointments to explain their concerns, to see their neurologist if their status changed, and that their health care providers communicated to coordinate their care. Participants who reported needing to see the provider but not having access or seeing the provider but would like to see them more often had lower HRQOL (ranging from -0.059 to -0.176) than participants who saw the provider as much as needed. Discussion: Gaps in access to care persist for people with MS in the United States and substantially affect HRQoL. Improving access to care for people with MS should be a health system priority.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".