The Relation between e-Health Literacy and Health-related Behaviors: A Systematic Review and Meta-analysis (Preprint)
Bibliographic record
Abstract
BACKGROUND With the widespread use of the Internet and mobile devices, many people gained improved access to obtain health-related information online for health promotion and disease management. As the acquired health information online can affect health-related behaviors, healthcare providers need to take into account each individual’s online health literacy (e-Health literacy, hereinafter “eHL”). OBJECTIVE To determine whether an individuals’ level of eHL affect actual health-related behaviors, the correlation between eHL and health-related behaviors was identified in an integrated manner through systematic literature review and meta-analysis. METHODS The MEDLINE, EMBASE, Cochrane, KoreaMed, and RISS databases were systematically searched for relevant studies published up to 19 March 2021 using combined keywords related to “e-Health” and “literacy.” A pooled correlation coefficient was generated by integrating the correlation coefficients obtained from the selected studies, and subgroup analysis with participants’ characteristics and types of behaviors was performed. The risk of bias was assessed using the modified Newcastle-Ottawa Scale. RESULTS Among 1,922 eHL-related papers, 14 studies that presented the correlation coefficient between eHL and health-related behaviors were included in the meta-analysis. The pooled correlation coefficient was 0.31 (95% confidence interval [CI]=0.25-0.34), which indicated a moderate correlation between eHL and health-related behaviors. In the subgroup analysis, the pooled correlation coefficient was 0.37 (95% CI=0.29-0.44) among older adults (aged≥65), 0.28 (95% CI=0.17-0.39) in the population with disease, and 0.36 (95% CI=0.27-0.41) for studies on the relevance to health-promoting behavior. CONCLUSIONS Our results of positive correlation between eHL and health-related behaviors indicate that eHL can be a mediator in the process by which health-related information leads to changes in health-related behavior. Larger-scale studies with stronger validity are needed to evaluate the detailed relationship between the proficiency level of eHL and health-related behaviors for health promotion in the future.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".