ArticlePEC innovation2025
Factors predicting eHealth literacy: A profile of California, USA.
Article in PEC innovation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Association between social capital, eHealth literacy, and digital health literacy among undergraduate students in China: a multigroup analysis based on gender differences.Frontiers in public health · 2026Article
- Digital Health Literacy and Self-Reported Health in Older Adults: The Role of Social Support in Internet Use.Journal of primary care & community healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Given the known benefits of enhanced eHealth literacy in promoting patient health, identifying segments of the patient population at risk for low eHealth literacy can provide avenues for health professionals to promote enhanced eHealth skills through targeted outreach and intervention. The purpose of this study was to examine factors associated with eHealth literacy and to identify disparities in eHealth literacy in California. Methods: Using survey data from the 2020 CALSPEAKS survey, ordinary least squares regression was performed on measures of self-assessed eHealth literacy to identify associated factors ( Results: Findings showed that the strongest and most consistent predictors of eHealth literacy included self-rated health, a proxy measure for religiosity, Internet use characteristics, and personality traits (i.e., conscientiousness, neuroticism). Conclusion: Overall, Californians report relatively high levels of eHealth literacy - but, those seeking to increase eHealth literacy among patients in the state may benefit from tailoring digital health information and eHealth interventions based on the significant associations found. Innovation: Few studies have examined the impacts of personality characteristics on eHealth literacy using a large sample - findings elucidate innovative pathways to enhancing eHealth literacy (e.g., accommodating online content or training interventions to those reporting low conscientiousness or high neuroticism).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.