Health Literacy Association With Health Behaviors and Health Care Utilization in Multiple Sclerosis: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Low health literacy is generally associated with poor health outcomes; however, health literacy has received little attention in multiple sclerosis (MS). OBJECTIVE: The aim of this study was to investigate the health literacy of persons with MS using the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry. METHODS: In 2012, we conducted a cross-sectional study of health literacy among NARCOMS participants. Respondents completed the Medical Term Recognition Test (METER) which assesses the ability to distinguish medical and nonmedical words, and the Newest Vital Sign (NVS) instrument which evaluates reading, interpretation, and numeracy skills. Respondents reported their sociodemographic characteristics, health behaviors, comorbidities, visits to the emergency room (ER), and hospitalizations in the last 6 months. We used logistic regression to evaluate the characteristics associated with functional literacy, and the association between functional literacy and health care utilization. RESULTS: Of 13,020 eligible participants, 8934 (68.6%) completed the questionnaire and were US residents. Most of them performed well on the instruments with 81.04% (7066/8719) having functional literacy on the METER and 74.62% (6666/8933) having adequate literacy on the NVS. Low literacy on the METER or the NVS was associated with smoking, being overweight or obese (all P<.001). After adjustment, low literacy on the METER was associated with ER visits (OR 1.28, 95% CI 1.10-1.48) and hospitalizations (OR 1.19, 95% CI 0.98-1.44). Findings were similar for the NVS. CONCLUSIONS: In the NARCOMS cohort, functional health literacy is high. However, lower levels of health literacy are associated with adverse health behaviors and greater health care utilization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".