Disparities in Telehealth Care in Multiple Sclerosis
Bibliographic record
Abstract
Background and Objectives: The COVID-19 pandemic has dramatically increased telehealth use. We assessed access to and use of telehealth care, including videoconferencing and usability of videoconferencing, among persons with multiple sclerosis (MS). Methods: In Fall 2020, we surveyed participants in the North American Research Committee on Multiple Sclerosis Registry. Participants reported availability and receipt of MS care or education through telehealth. Participants who completed ≥1 live videoconferencing visit completed the Telehealth Usability Questionnaire (TUQ). We tested factors associated with access to and receipt of telehealth care using logistic regression. We tested factors associated with TUQ scores using quantile regression. Results: Of the 8,434 participants to whom the survey was distributed, 6,043 responded (71.6%); 5,403 were eligible for analysis. Of the respondents, 4,337 (80.6%) were women, and they had a mean (SD) age of 63.2 (10.0) years. Overall, 2,889 (53.5%) reported access to MS care via telehealth, and 2,110 (39.1%) reported receipt of MS care via telehealth including 1,523 (28%) via videoconference. Among participants who reported telehealth was available, older age was associated with decreased odds of having a telehealth video visit; higher income and being physically active were associated with increased odds. Older age and moderate to very severe visual symptoms were associated with lower perceived usability of telehealth. Discussion: Older age, lower socioeconomic status, and disease-related impairments are associated with less access to and use of telehealth services in people with MS. Barriers to telehealth should be addressed to avoid aggravating health care disparities when using digital medicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".