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Record W4220834683 · doi:10.2196/37687

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

2022· article· en· W4220834683 on OpenAlexvenueno aff
Enmanuel A. Chavarria, Shannon M. Christy, Han Feng, Hongyu Miao, Liliana Gutierrez, Diana Lopez, Julian Sanchez, Clement K. Gwede, Cathy D. Meade

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
FundersNational Cancer InstituteFlorida Department of Health
KeywordseHealthHealth literacyMedicineDescriptive statisticsHealth careLiteracyFamily medicineGerontologyPsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.074
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0740.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0020.003
Science and technology studies0.0030.000
Scholarly communication0.0000.005
Open science0.0010.002
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.099
GPT teacher head0.541
Teacher spread0.442 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2022
Admission routes1
Has abstractyes

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