Barriers and facilitators to the future uptake of regular COVID-19 booster vaccinations among young adults in the UK
Bibliographic record
Abstract
The evidence of waning immunity offered by COVID-19 vaccines suggests that widespread and regular uptake of routine COVID-19 booster vaccines will be needed. In order to understand the hesitancy toward COVID-19 boosters, we examined the barriers and facilitators to receiving regular COVID-19 boosters in a sample of young adults in the UK. A cross-sectional survey was completed by 423 participants (M = 22.8; SD = 8.6 years) and assessed intention to receive regular COVID-19 boosters, the 7C antecedents of vaccination (i.e. confidence, complacency, constraints, calculation, collective responsibility, and compliance and conspiracy), and any previous experience of side-effects from COVID-19 vaccines. Participants also provided a free text qualitative response outlining their barriers and facilitators to receiving regular COVID-19 boosters. Overall, 42.8% of the sample were hesitant about receiving regular COVID-19 boosters. Multivariate logistic regression analysis showed that intention to accept future boosters was associated with having higher levels of confidence in, and compliance with, vaccines, lower levels of complacency, calculation and perceptions of constraints to vaccination, and having experienced less severe side effects from the COVID-19 vaccines. Qualitative responses highlighted the main barriers included experiencing side effects with previous COVID-19 vaccines and inaccessibility of vaccination services. Key facilitators included protecting the health of friends and family members, protecting personal health, and maintaining regular activities. Our findings suggest that interventions targeted at increasing booster uptake should address the experience of side effects while also emphasizing the positive vaccine benefits relating to the individual's health and the maintenance of their regular work and social activities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".