What motivates adults to accept influenza vaccine? An assessment of incentives, ease of access, messaging, and sources of information using a discrete choice experiment
Bibliographic record
Abstract
Seasonal influenza vaccination rates remain low, and contribute to preventable influenza cases, hospitalizations, and deaths in the US. While numerous interventions have been implemented to increase vaccine uptake, there is a need to determine which interventions contribute most to vaccine willingness, particularly among age groups with vaccination rates that have plateaued at suboptimal levels. This study aimed to quantify the relative effect of multiple interventions on vaccine willingness to receive influenza vaccine in three age groups using a series of hypothetical situations with different behavioral interventions. We assessed the relative impact of four categories of interventions: source of vaccine messages, type of vaccination messages, vaccination incentives, and ease of vaccine access using a discrete choice experiment. Within each category, we investigated the role of four different attributes to measure their relative contribution to willingness to be vaccinated by removing one option from each of the intervention categories. Among the 1,763 Minnesota residents who volunteered for our study, participants expressed vaccine willingness in over 80% of the scenarios presented. Easy access to drop-in vaccination sites had the greatest impact on vaccine willingness in all age groups. Among the younger age group, small financial incentives also contributed to high vaccine willingness. Our results suggest that public health programs and vaccination campaigns may improve their chances of successfully increasing vaccine willingness if they offer interventions preferred by adults, including facilitating convenient access to vaccination and offering small monetary incentives, particularly for young adults.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".