Online Health Information Seeking and eHealth Literacy Among Spanish Language–Dominant Latino Adults Receiving Care in a Community Clinic: Secondary Analysis of Pilot Randomized Controlled Trial Data
Bibliographic record
Abstract
BACKGROUND: eHealth literacy is the ability to seek, obtain, and decipher online health information (OHI) for health and disease management. Rapid developments in eHealth (eg, health care services and online information) place increased demands on patients to have high eHealth literacy levels. Yet, greater emphasis on eHealth may disproportionately affect groups with limited eHealth literacy. Cultural background, language, and eHealth literacy are influential considerations affecting health care and information access, health care use, and successful eHealth resource use, and they may influence OHI seeking for behavioral change toward cancer prevention. OBJECTIVE: This study aimed to characterize the extent of OHI seeking and eHealth literacy among Spanish-dominant (SD) Latino adults aged 50 to 75 years. Further, we aimed to examine potential associations between sociodemographic characteristics, Preventive Health Model (PHM) constructs, OHI-seeking behaviors, and eHealth literacy, separately. METHODS: Participants (N=76) self-identified as Latino, were enrolled in a colorectal cancer (CRC) screening intervention, were aged 50 to 75 years, were at average risk for CRC, were not up to date with CRC screening, and preferred receiving health information in Spanish. We describe participants' sociodemographic characteristics, PHM constructs, OHI-seeking behaviors, and eHealth literacy-among those seeking OHI-assessed at enrollment. Descriptive analyses were first performed for all variables. Next, primary univariate logistic analyses explored possible associations with OHI seeking. Finally, using data from those seeking OHI, exploratory univariate analyses sought possible associations with eHealth literacy. RESULTS: A majority (51/76, 67%) of the participants were female, 62% (47/76) reported not having graduated high school, and 41% (31/76) reported being unemployed or having an annual income of less than US $10,000. Additionally, 75% (57/76) of the participants reported not having health insurance. In total, 71% (54/76) of the participants reported not having sought OHI for themselves or others. Univariate logistic regression suggested that higher educational attainment was significantly associated with an increased likelihood of having sought OHI (odds ratio 17.4, 95% CI 2.0-150.7; P=.009). Among those seeking OHI (22/76, 29%), 27% (6/22) were at risk of having low eHealth literacy based on an eHealth Literacy Scale score of less than 26. Among OHI seekers (22/76, 29%), an examination of associations found that higher eHealth literacy was associated with greater self-efficacy for screening with the fecal immunochemical test (β=1.20, 95% CI 0.14-2.26; P=.02). CONCLUSIONS: Most SD Latino participants had not sought OHI for themselves or others (eg, family or friends), thus potentially limiting access to beneficial online resources. Preliminary findings convey that higher eHealth literacy occurs among those with higher self-efficacy for CRC screening. Findings inform areas of focus for future larger-scale investigations, including further exploration of reasons for not seeking OHI among SD Latino adults and an in-depth look at eHealth literacy and cancer screening behaviors. TRIAL REGISTRATION: ClinicalTrials.gov NCT03078361; https://clinicaltrials.gov/ct2/show/NCT03078361.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".