Building Vaccine Readiness for Future Pandemics: Insights from COVID-19 Vaccine Intent and Uptake
Bibliographic record
Abstract
Background/Objectives: This longitudinal study investigated psychosocial predictors of COVID-19 vaccine intentions before vaccine availability (July 2020) and vaccine uptake or ongoing intent after widespread vaccine rollout (April 2021) using constructs from the Health Belief Model (HBM) and a measure of trust in government. Methods: A U.S. adult sample (N = 142) completed surveys at two time points: prior to and following the release of COVID-19 vaccines. Key predictors included demographics, trust in government, and HBM constructs. Hierarchical logistic regression was used to predict vaccine intent and uptake at both time points. Results: At Time 1, intent to vaccinate was significantly predicted by higher perceived susceptibility (p = 0.038), greater perceived benefits (p < 0.001), and lower perceived barriers (p = 0.002). Trust in government was not a significant predictor. At Time 2, vaccine uptake/ongoing intent was significantly predicted by higher trust in government (p = 0.047), greater perceived benefits (p < 0.001), and lower perceived barriers (p = 0.002). Perceived susceptibility was no longer a significant predictor. Between time points, trust in government and self-efficacy increased, while perceived severity and barriers decreased. Conclusions: Perceived benefits and barriers were robust predictors of vaccine behavior across both time points. Trust in government became a stronger predictor once vaccines were available, underscoring the importance of building and maintaining public trust throughout a health crisis. Messaging should emphasize vaccine benefits, proactively address barriers, and adapt over time as public perceptions shift. These findings inform strategies for enhancing vaccine confidence and readiness in future pandemics.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".