Understanding Health Information Seeking on the Internet Among Sexual Minority People: Cross-Sectional Analysis From the Health Information National Trends Survey
Bibliographic record
Abstract
BACKGROUND: Individuals who face barriers to health care are more likely to access the Internet to seek health information. Pervasive stigma and heterosexism in the health care setting are barriers to health care for sexual minority people (SMP, ie, lesbian, gay, and bisexual people); therefore, SMP may be more likely to use the Internet as a source of health information compared to heterosexual people. OBJECTIVE: Currently, there is a dearth of published empirical evidence concerning health information seeking on the Internet among SMP; the current project addresses this gap. METHODS: Data from the 2015 Health Information National Trends Survey Food and Drug Administration Cycle were used to describe and summarize health information seeking among SMP (n=105) and heterosexual people (n=3405). RESULTS: Almost all of the SMP in this sample reported having access to the Internet (92.4%, 97/105). SMP were equally as likely as heterosexual people to seek health information on the Internet (adjusted odds ratio [aOR] 0.94, 95% CI 0.56-1.66) and to report incidental exposure to health information online (aOR 1.02, 95% CI 0.66-1.60). SMP were 58% more likely to watch a health-related video on YouTube than heterosexual people (aOR 1.58, 95% CI 1.00-2.47). Incidental exposure to health information was associated with seeking health information for oneself (aOR 3.87, 95% CI 1.16-14.13) and for someone else (aOR 6.30, 95% CI 2.40-17.82) among SMP. CONCLUSIONS: SMP access the Internet at high rates and seek out health information online. Their incidental exposure could be associated with seeking information for self or others. This suggests that online interventions could be valuable for delivering or promoting health information for SMP.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".