Low-Level Evidence Suggests that Perceived Ability to Evaluate and Trust Online Health Information is Associated with Low Health Literacy
Bibliographic record
Abstract
Objective – To review, based on research evidence, the correlation between low health literacy and four outcomes of interest: (1) the ability to evaluate online health information based on (2) perceived reliability and accuracy, (3) trust in the Internet as an information source, and (4) the application of established evaluation criteria. Design – Systematic review and narrative synthesis. Setting –MEDLINE, PsycInfo, Web of Science, CINAHL, and Communication and Mass-media Complete as well as articles discovered through the snowball method. Subjects – 38 studies identified through a systematic literature search. Methods – An exhaustive list of potential articles was gathered through searching five online databases and Google Scholar, and hand searching of references. Inclusion and exclusion criteria were applied in a two-phase screening process in which two researchers participated to address reliability. Data, including study characteristics and metadata, predictors, assessment methods, and outcomes, were extracted from relevant studies, and then synthesized narratively. Main Results – Following duplication removal 13,632 records were retrieved, 254 of which were identified for full-text assessment. Thirty-eight studies met the eligibility criteria. All studies were non-experimental and therefore graded as a low level of evidence; 35 were cross-sectional designs, 1 a focus group, and 2 were observational studies. Studies varied widely in population definition and sample size and were published between 2001 and 2013, primarily in North America. Overall, a positive association was identified between health literacy and outcomes related to the ability to evaluate or trust Internet health information, while findings were inconsistent related to perceived quality of information and the application of evaluative criteria. Four studies examined the impact of health literacy levels on one or more of the outcomes of interest. The most prevalent outcome measure studied was trust in online health information, and the least prevalent was the use of evaluative criteria. The ability to trust online health information was assessed primarily through self-reporting, half of which utilized the eHealth Literacy scale, the majority of which indicated a positive correlation between education level or low health literacy and the perceived or actual ability to evaluate online health information. No studies on perceived information quality were found to utilize health literacy as an indicator. A positive association between educational level and trust in health information on the Internet was reported in ten studies, while two articles noted a similar correlation based on proxies for health literacy, including English language proficiency and comprehension comfort. In terms of the use of evaluation criteria, only one study focused on health literacy, indicating that those with low health literacy evaluate online health information based on search result placement, celebrity endorsement, image quality, and site authorship, and that they trust university researchers more than government or religious authorities to provide health information. No association was shown between readability or physician-provided online information and evaluation criteria while one study demonstrated that study participants with higher education tended to check author credentials more often when evaluating a website. Conclusion – Effective and informed evaluation of online health information is impacted by low health literacy.
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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 | 0.031 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".