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Record W4313196631 · doi:10.2196/39891

The Factors Associated With Confidence in Using the Internet to Access Health Information: Cross-sectional Data Analysis

2022· article· en· W4313196631 on OpenAlexvenueno aff
Kasi Lou Van Heel, Anna Nelson, Daniel Handysides, Huma Shah

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsHealth Information National Trends SurveyOdds ratioConfidence intervalThe InternetMedicineHealth informationCross-sectional studyOddsHealth equityPopulationHealth careEnvironmental healthPublic healthGerontologyDemographyLogistic regressionNursingComputer sciencePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Confidence in health information access is a measure of the perceived ability to obtain health information. One's beliefs or perceived ability to access health information is particularly important in understanding trends in health care access. Previous literature has found that access to health information is lowest among society's most vulnerable population groups. These groups include older, less educated, and low-income populations. While health confidence has previously been used as a scale to measure health outcomes, additional research is needed describing the demographic factors associated with users' confidence in health information access. This may be a key component of health information seeking that affects beneficial health outcomes such as prevention and treatment. OBJECTIVE: This study examines the demographic factors associated with the levels of confidence in using the internet to access health information for adults 18 years and older in the United States. METHODS: Using a cross-sectional design, secondary data from the Health Information National Trends Survey (HINTS) 5, Cycle 3 (2019) were analyzed (N=5374). An ordinal regression stratified by internet use was used to determine the association between demographic characteristics and level of confidence in health information access. RESULTS: When the internet is the primary source for health information, high school graduates (adjusted odds ratio [AOR] 0.58, 95% CI 0.37-0.89) compared to those with a college degree or more had significantly lower odds of being confident in obtaining health information. In addition, non-Hispanic Asian participants (AOR 0.44, 95% CI 0.24-0.82) compared to non-Hispanic White participants, male participants (AOR 0.72, 95% CI 0.54-0.97) compared to female participants, and those who made between US $20,000-$35,000 annually (AOR 0.55, 95% CI 0.31-0.98) compared to those who made US $75,000 or more annually had significantly lower odds of being confident in obtaining health information via the internet. Moreover, when the internet is the primary source for health information, those with health insurance had significantly higher odds of being confident in obtaining health information (AOR 2.91, 95% CI 1.58-5.34) compared to those who do not have health insurance. Lastly, a significant association was observed between confidence in health information access and primary health information source and frequency of visiting a health care provider. CONCLUSIONS: Confidence in accessing health information can differ by individual demographics. Accessing health-related information from the internet has become increasingly more common and can provide insight into health information-seeking behaviors. Further exploration of these factors can inform the science of health education by providing deeper insight into improving access to health information for vulnerable populations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.430
GPT teacher head0.635
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2022
Admission routes1
Has abstractyes

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