The level of electronic health literacy among older adults: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: In the context of deeper integration of the internet and healthcare services, eHealth literacy levels have become an important predictor of public health outcomes and health-promoting behaviors. However, there is a lack of comprehensive understanding of eHealth literacy levels among older adults. OBJECTIVE: To systematically assess the level of eHealth literacy among older adults. METHODS: We conducted searches in MEDLINE, Embase, Web of Science, CINAHL, PsycINFO, China National Knowledge Infrastructure Database (CNKI), Wanfang Database, Weipu Database (VIP), and Chinese Biomedical Database (Sinomed) to collect survey studies on the eHealth literacy levels of the older adults, with a search timeframe from the establishment of the database to May 2024. The quality of the included literature was assessed using the Agency for Healthcare Research and Quality (AHRQ) and the Newcastle-Ottawa Scale (NOS). Additionally, subgroup analysis and meta-regression were conducted to detect sources of heterogeneity. Funnel plots and Egger's test were used to assess publication bias. RESULTS: A total of 48 relevant studies were included, including 45 cross-sectional, 2 cohort studies and 1 longitudinal study, comprising 33,919 older adults. The quality of the studies was all above moderate, with 10 high-quality publications. Meta-integration results showed that the eHealth literacy score of older adults was 21.45 (95% CI:19.81-23.08). Subgroup analysis showed that among the elderly population, females had lower eHealth literacy at 19.13 (95% CI:15.83-22.42), those aged 80 years and older had lower eHealth literacy at 16.55 (95% CI:11.73-21.38), and elderly individuals without a spouse and living alone had even lower eHealth literacy at 18.88 (95% CI:15.71-22.04) and 16.03 (95% CI:16.51-21.79). Based on region, eHealth literacy was lower among older adults in developing countries at 20.71 (95% CI:18.95-22.48). Meta-regression results indicate that sample size and region can significantly impact heterogeneity. CONCLUSION: Our results found that the average eHealth literacy score of the elderly was 21.45, which was much lower than the passing level (≥ 32), suggesting that more attention should be paid to the eHealth literacy aspect of the elderly. Meanwhile, due to the limitation of the literature sources, the global representativeness of the results of this study still needs to be supported by more research data from other countries.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".