Urban-Rural Differences in the Association of eHealth Literacy With Medication Adherence Among Older People With Frailty and Prefrailty: Cross-Sectional Study
Bibliographic record
Abstract
Background: With advances in science and technology and improvements in health literacy, more studies have focused on frailty prevention by promoting medication adherence, emphasizing the role of eHealth literacy. However, the association between eHealth literacy and medication adherence in frail older adults has not been well studied, and it is unknown whether urban-rural differences exist in this relationship. Objective: This study aims to examine the relationship between eHealth literacy and medication adherence in older people with different frailty statuses, emphasizing variations between rural and urban areas. Methods: Between November and December 2020, a total of 4218 urban and rural community members (aged ≥60 years) in China were recruited as participants using a multistage random sampling method. A face-to-face structured questionnaire survey was conducted to collect information on demographic characteristics, eHealth literacy (consisting of application, evaluation, and decision dimensions), and medication adherence. eHealth literacy was assessed using the Chinese version of the eHealth Literacy Scale developed by Norman and Skinner, and medication adherence was measured using the 4-item Morisky scale. We used a general descriptive analysis and stratified logistic regression models to examine how eHealth literacy is linked to medication adherence and urban-rural differences. Results: There were 4218 respondents, of which 2316 (54.9%) lived in urban areas and 1902 (45.1%) in rural areas, respectively. After adjusting for potential confounders, among participants with prefrailty, eHealth literacy was associated with medication adherence in urban areas in terms of less application (adjusted odds ratio [AOR] 1.16, 95% CI 0.82-1.63), less evaluation (AOR 1.29, 95% CI 0.92-1.81), and less decision ability (AOR 1.20, 95% CI 0.86-1.68); eHealth literacy was linked with medication adherence in the rural areas in terms of less application (AOR 1.10, 95% CI 0.56-2.13), less evaluation (AOR 1.05, 95% CI 0.61-1.79), and less decision ability (AOR 1.10, 95% CI 0.64-1.90). Among frail participants, less eHealth literacy (AOR 0.85, 95% CI 0.48-1.51), along with its dimensions, including less application (AOR 0.85, 95% CI 0.47-1.54), evaluation (AOR 0.89, 95% CI 0.50-1.57), and decision ability (AOR 0.99, 95% CI 0.55-1.76), were associated with medication adherence in urban areas; less eHealth literacy (AOR 0.89, 95% CI 0.48-1.65), along with its dimensions, including less application (AOR 1.23, 95% CI 0.62-2.44), evaluation (AOR 0.98, 95% CI 0.53-1.82), and decision ability (AOR 0.90, 95% CI 0.49-1.67), were associated with medication adherence in rural areas. Conclusions: The results of this study suggest that there is an association between eHealth literacy and medication adherence among older people with frailty and prefrailty. To promote medication adherence, eHealth literacy can be helpful in tailoring interventions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".