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Record W2785970163 · doi:10.2196/publichealth.6260

Trust in Health Information Sources: Survey Analysis of Variation by Sociodemographic and Tobacco Use Status in Oklahoma

2018· article· en· W2785970163 on OpenAlexvenueno aff
Cati Brown‐Johnson, Lindsay Boeckman, Ashley H. White, Andrea Burbank, Sjonna Paulson, Laura A. Beebe

Bibliographic record

VenueJMIR Public Health and Surveillance · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Heart, Lung, and Blood Institute
KeywordsMass mediaSocial mediaPublic healthPsychological interventionHealth communicationInternet privacyPopulationMedicineThe InternetMoresPsychologyEnvironmental healthPublic relationsAdvertisingBusinessPolitical scienceNursingWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Modern technology (ie, websites and social media) has significantly changed social mores in health information access and delivery. Although mass media campaigns for health intervention have proven effective and cost-effective in changing health behavior at a population scale, this is best studied in traditional media sources (ie, radio and television). Digital health interventions are options that use short message service/text messaging, social media, and internet technology. Although exposure to these products is becoming ubiquitous, electronic health information is novel, incompletely disseminated, and frequently inaccurate, which decreases public trust. Previous research has shown that audience trust in health care providers significantly moderates health outcomes, demographics significantly influence audience trust in electronic media, and preexisting health behaviors such as smoking status significantly moderate audience receptivity to traditional mass media. Therefore, modern health educators must assess audience trust in all sources, both media (traditional and digital) and interpersonal, to balance pros and cons before structuring multicomponent community health interventions. OBJECTIVE: We aimed to explore current trust and moderators of trust in health information sources given recent changes in digital health information access and delivery to inform design of future health interventions in Oklahoma. METHODS: We conducted phone surveys of a cross-sectional sample of 1001 Oklahoma adults (age 18-65 years) in spring 2015 to assess trust in seven media sources: traditional (television and radio), electronic (online and social media), and interpersonal (providers, insurers, and family/friends). We also gathered information on known moderators of trust (sociodemographics and tobacco use status). We modeled log odds of a participant rating a source as "trustworthy" (SAS PROC SURVEYLOGISTIC), with subanalysis for confounders (sociodemographics and tobacco use). RESULTS: Oklahomans showed the highest trust in interpersonal sources: 81% (808/994) reported providers were trustworthy, 55% (550/999) for friends and family, and 48% (485/998) for health insurers. For media sources, 24% of participants (232/989) rated the internet as trustworthy, followed by 21% of participants for television (225/998), 18% for radio (199/988), and only 11% for social media (110/991). Despite this low self-reported trust in social media, 40% (406/991) of participants reported using social media for tobacco-related health information. Trust in health providers did not vary by subpopulation, but sociodemographic variables (gender, income, and education) and tobacco use status significantly moderated trust in other sources. Women were on the whole more trusting than men, trust in media decreased with income, and trust in friends and family decreased with education. CONCLUSIONS: Health education interventions should incorporate digital media, particularly when targeting low-income populations. Utilizing health care providers in social media settings could leverage high-trust and low-cost features of providers and social media, respectively.

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)0.016
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.048
GPT teacher head0.393
Teacher spread0.345 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations34
Published2018
Admission routes1
Has abstractyes

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