COVID-19 Information Sources and Vaccination Status Among Californian Adults by Generation Using the 2022 California Health Interview Survey: Cross-Sectional Study
Bibliographic record
Abstract
Background: As communication technology advances and the digital divide grows, a deeper understanding of the influence of different information sources on vaccine uptake by generations can inform targeted public health interventions in times of future crisis. While the COVID-19 pandemic highlighted the role of media sources on the decision to receive vaccines, no studies have focused on the impact of the type and number of information sources in a population-based sample in California. Objective: In this study, we examined associations between Californians' self-reported most relied upon COVID-19 information sources, categorized by type and measured as a count, and their COVID-19 vaccination status using data collected from the 2022 California Health Interview Survey. To address differences in information preferences and vaccine uptake by age, we also tested for potential effect modification of the relationship between relied upon COVID-19 information sources and vaccination status by generational membership (eg, Generation Z, millennials, Generation X, baby boomers, and Silent Generation). Methods: We conducted a secondary analysis of cross-sectional data from the 2022 California Health Interview Survey. Vaccine status (any or none) was modeled as a function of information sources (or count) controlling for important sociodemographic and health confounding variables. Interaction terms of information sources (or count) by generational status were added to the models to test effect modification, and if significant, the models were stratified by generation. All analysis was survey-weighted to account for the complex survey sampling design. Results: Compared to relying on traditional news media for COVID-19 information, relying on word of mouth (odds ratio [OR] 0.6), social media (OR 0.62), and doctors (OR 0.41) for COVID-19 information was associated with lower odds of being vaccinated for COVID-19. A dose-response relationship was identified, with each additional information source associated with 9% higher odds of being vaccinated for COVID-19. In stratified models, social media, compared to traditional news media, was associated with lower odds of vaccination for Generation X, baby boomers, and the Silent Generation. Conclusions: Health information preferences, especially for traditional news media, are associated with COVID-19 vaccine uptake, and the information sources differ by generation. These findings provide information for stakeholders interested in vaccine hesitancy, health informatics, messaging strategies, health literacy, and future health information outreach programs during epidemics or pandemics. Dissemination of public health information should include multiple information sources to reach all individual preferences across different generations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".