Determinants of health literacy and its impact on illness perception in patients with multiple sclerosis: evidence from patient-reported outcomes
Bibliographic record
Abstract
Background: Health literacy (HL) is a key determinant of health outcomes, especially in chronic neurological diseases such as multiple sclerosis (MS). Insufficient HL may impair the ability of patients to manage their condition, reduce treatment adherence and increase the use of healthcare. Objective: To identify factors influencing HL among individuals with MS and to explore its association with illness perception and medication-related behaviours. Methods: Between April and September 2023, we consecutively enrolled 330 patients with MS from a single outpatient clinic. We included individuals aged 18-65 years with functional literacy, and we did not exclude participants based on MS subtype, education level, disability status or treatment characteristics. We assessed HL using the Newest Vital Sign, cognition using the Montreal Cognitive Assessment (MoCA), emotional status using the Hospital Anxiety and Depression Scale, and illness perception using the Brief Illness Perception Questionnaire (BIP-Q). We also evaluated self-reported medication adherence and perceived treatment benefits. After excluding 11 participants with incomplete data, we analysed 319 complete responses in accordance with Strengthening the Reporting of Observational Studies in Epidemiology guidelines. Results: Overall, 49.7% of participants demonstrated adequate HL. The HL correlated positively with MoCA scores and education (path coefficients: 0.117, 0.114) and negatively with disease duration, age and depression (-0.023,-0.029, -0.085). HL was positively associated with illness perception (BIP-Q coefficient: 1.558). The model explained 35.6% of the variance in HL and 5.7% in illness perception (R²=0.356; 0.057). Conclusion: Our findings suggest that routine HL assessment and targeted educational interventions may enhance understanding, adherence and informed decision-making, ultimately improving disease management and outcomes in MS.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".