Factors associated with eHealth literacy among people with systemic sclerosis: A Scleroderma Patient-centred Intervention Network (SPIN) Cohort cross-sectional study
Bibliographic record
Abstract
Introduction/objective: eHealth literacy reflects the ability to obtain, understand, and evaluate health information from electronic sources and apply this information to health problems. Our objective was to evaluate sociodemographic (age, sex, race or ethnicity, education, marital status, country, residence location) and disease factors (duration, subtype) associations with eHealth literacy among individuals with systemic sclerosis (SSc). Methods: Scleroderma Patient-centred Intervention Network (SPIN) Cohort participants completed the 8-item eHealth Literacy Scale (eHEALS) from January 17 to February 18, 2025. Multivariable linear regression was used to assess associations of sociodemographic and disease characteristics with eHealth literacy. Results: The 333 participants were from France (N = 116, 35%), Canada (N = 90, 27%), the United States (N = 85, 26%), the United Kingdom (N = 32, 10%), and Australia, Mexico, or Spain (N = 10, 3%). Most participants were female (N = 295, 89%), White (N = 268, 80%), and had limited SSc (N = 206, 62%). Compared to the United States, participants from Canada (-2.2 points, 95% CI -4.2 to -0.1; standardized mean difference (SMD) = -0.33) and France (-4.2 points, 95% CI -6.2 to -2.3; SMD = -0.64) had significantly lower eHEALS scores. Age, sex, race or ethnicity, marital status, education level, time since first non-Raynaud's symptom onset, and disease subtype were not associated with eHEALS scores. Conclusion: eHealth literacy in SSc was not associated with age and education level as in some other studies but was associated with country. Future research should examine country-level differences in eHealth literacy for individuals with SSc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".