Evidence map›Paper›PMID 40678748›Full record

ArticlePEC innovation2025

Factors predicting eHealth literacy: A profile of California, USA.

Ronald W Berkowsky, Brandon Almanza, Jasmine Garcia, Noah Graza

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ronald W BerkowskyCalifornia State University Channel Islands, 1 University Dr., Camarillo, CA 93012, United States of America.
Brandon AlmanzaWest Coast University, United States of America.
Jasmine GarciaSan Diego State University, United States of America.
Noah GrazaWest Coast University, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Computer literacyeHealtheHealth literacyHealth literacyInformation and communication technologies (ICTs)Information literacyInternet

Identifiers

PMID40678748
PMCPMC12270070

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.