Individual determinants of COVID-19 vaccine hesitancy
Bibliographic record
Abstract
BACKGROUND: Novel coronavirus disease 2019 (COVID-19) vaccine hesitancy is a barrier to achieving herd immunity, and thus, a prominent public health concern. This study aimed to identify the determinants of COVID-19 vaccine hesitancy based on the World Health Organization's '3Cs' model (i.e., confidence, complacency, and convenience) in the United States (U.S.) and Canada. METHODS: Data from 7678 adults ages 18 or older were collected from the four most populous U.S. States, specifically New York, California, Florida, and Texas, and from English-speaking Canada at three timepoints, in May and July 2020, and March 2021 using a web-based survey (www.covid19-database.com). Sociodemographic information was collected, and comprehensive psychological assessments were administered. Univariate analyses were performed to identify the individual determinants of vaccine hesitancy, which were categorized as: 1) vaccine confidence, 2) vaccine complacency, 3) sociodemographic, and 4) other psychological factors. A series of models were computed using these categorizations. RESULTS: Mistrust of vaccine benefit (β(SE) = 0.67(0.01), p<0.001, partial η2 = 0.26) and lower perceived seriousness of COVID-19 (β(SE) = 0.68(0.02), p<0.001, partial η2 = 0.12) were the principal determinants of vaccine hesitancy. Right-wing political affiliation (β(SE) = 0.32(0.02), p<0.001, partial η2 = 0.03), higher risk propensity (β(SE) = 0.24(0.02), p<0.001, partial η2 = 0.03), and less negative mental health effects of the COVID-19 pandemic (β(SE) = 0.20(0.01), p<0.001, partial η2 = 0.03) were the main sociodemographic and psychological determinants. Other sociodemographic determinants included younger age, women, race, and employment status. Lack of vaccine confidence and complacency explained 38% and 21% of the variance in vaccine hesitancy, respectively; whereas, sociodemographic and psychological determinants explained 13% and 11% of the variance in vaccine hesitancy, respectively. DISCUSSION: Targeted and tailored public health interventions that enhance the public's confidence in vaccines and emphasize the risk and seriousness of COVID-19 may address COVID-19 vaccine hesitancy. Efforts directed toward specific marginalized and underserved groups may be required to promote vaccine confidence.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".