Vaccinomics: a cross-sectional survey of public values
Bibliographic record
Abstract
Objective: We characterize public values regarding vaccinomics, which aims to improve vaccine safety and effectiveness using genomics.Methods: Panel survey (2020) of ≥18-year-olds with embedded animation introduced vaccinomics. Sociodemographic, health, and vaccination-related items were adapted from validated scales. Novel items measured trust in public health authorities, vaccinomics-related values, and preferences for federal funding: vaccinomics compared with vaccine issues and chronic diseases. Beginning and end of survey confidence in vaccine safety was measured to assess potential changes. Data were weighted to the U.S. Census. Vaccinomics-related concerns were stratified by sociodemographic characteristics, vaccine hesitancy status (composite outcome), reported serious vaccine reactions, and trust in public health authorities (PHA). Log binomial regression models estimated associations between these variables and agency to make vaccine-related decisions.Results: Most (70.7%, N = 1,925) respondents expected vaccinomics would increase their vaccine confidence compared to now. Agreement was highest among those without serious vaccine reaction experience (unexperienced: 74.2% versus experienced: 62.3%), with high trust in PHA (high: 83.3% versus low: 57.4%), and low vaccine hesitancy among parents of teenagers (low: 78.8% versus high: 62.5%) and adults without minor children (low: 79.8% versus high: 60.6%; all p < .01). Belief that vaccination was an individual’s choice was associated with reported serious reactions (adjusted Prevalence Ratio (aPR): 1.16; 95% CI: 1.07, 1.25) and low trust (aPR: 0.91; 0.84, 0.98). Beginning versus end of survey vaccine safety perceptions were similar.Conclusion: Federal funding, communications, and policies should assure the public that vaccinomics will not remove their decision–making power and engender trust in PHA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".