Predictors of Vaccine Hesitancy: Implications for COVID-19 Public Health Messaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Successful immunization programs require strategic communication to increase confidence among individuals who are vaccine-hesitant. This paper reviews research on determinants of vaccine hesitancy with the objective of informing public health responses to COVID-19. METHOD: A literature review was conducted using a broad search strategy. Articles were included if they were published in English and relevant to the topic of demographic and individual factors associated with vaccine hesitancy. RESULTS AND DISCUSSION: Demographic determinants of vaccine hesitancy that emerged in the literature review were age, income, educational attainment, health literacy, rurality, and parental status. Individual difference factors included mistrust in authority, disgust sensitivity, and risk aversion. CONCLUSION: Meeting target immunization rates will require robust public health campaigns that speak to individuals who are vaccine-hesitant in their attitudes and behaviours. Based on the assortment of demographic and individual difference factors that contribute to vaccine hesitancy, public health communications must pursue a range of strategies to increase public confidence in available COVID-19 vaccines.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it