Web-Based Health Information Seeking Among African American and Hispanic Men Living With Chronic Conditions: Cross-sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Previous research has identified disparities in seeking and using web-based health information to inform health-related behaviors. Relatively few studies however have examined the correlations between web-based health information seeking and use based on race, gender, age, and the presence of chronic health conditions. OBJECTIVE: In this study, we identify factors associated with seeking and using web-based health information among a uniquely vulnerable and intersectional population-middle-aged and older (40 years and older) African American and Hispanic men living with one or more chronic conditions. METHODS: Survey responses were collected from a purposive sample of African American and Hispanic men using Qualtrics web-based survey management software. To qualify for inclusion in the study, respondents had to identify as African American or Hispanic men, report having at least one chronic condition, and be aged 40 years and older. A series of binary logistic regression models was created using backward elimination. Statistical significance was determined at P<.05 for all analyses. RESULTS: Web-based health information seeking among African American and Hispanic men is a function of education, the presence of multiple chronic conditions, frustration with health care providers, internet use, and the perceived reliability of web-based health information. The use of web-based health information to inform interactions with health care providers was more common among African American and Hispanic men, who rated their health as relatively good, perceived barriers to care, used technology regularly, and took more daily medications. CONCLUSIONS: Understanding the factors that influence African American and Hispanic men seeking web-based health information may help improve the care and treatment of chronic conditions. African American and Hispanic men seek web-based health information as a substitute for routine care and to inform their discussions with health care providers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".