Hesitancy for receiving regular SARS-CoV-2 vaccination in UK healthcare workers: a cross-sectional analysis from the UK-REACH study
Bibliographic record
Abstract
Abstract Background Regular vaccination against SARS-CoV-2 may be needed to maintain immunity in ‘at-risk’ populations, which include healthcare workers (HCWs). However, little is known about the proportion of HCWs who might be hesitant about receiving a hypothetical regular SARS-CoV-2 vaccination or the factors associated with this hesitancy. Methods Cross-sectional analysis of questionnaire data collected as part of UK-REACH, a nationwide, longitudinal cohort study of HCWs. The outcome measure was binary, either a participant indicated they would definitely accept regular SARS-CoV-2 vaccination if recommended or they indicated some degree of hesitancy regarding acceptance (probably accept or less likely). We used logistic regression to identify factors associated with hesitancy for receiving regular vaccination. Results A total of 5454 HCWs were included in the analysed cohort, 23.5% of whom were hesitant about regular SARS-CoV-2 vaccination. Black HCWs were more likely to be hesitant than White HCWs (aOR 2.60, 95%CI 1.80–3.72) as were those who reported a previous episode of COVID-19 (1.33, 1.13–1.57 [vs those who tested negative]). Those who received influenza vaccination in the previous two seasons were over five times less likely to report hesitancy for regular SARS-CoV-2 vaccination than those not vaccinated against influenza in either season (0.18, 0.14–0.21). HCWs who trusted official sources of vaccine information (such as NHS or government adverts or websites) were less likely to report hesitancy for a regular vaccination programme. Those who had been exposed to information advocating against vaccination from friends and family were more likely to be hesitant. Conclusions In this study, nearly a quarter of UK HCWs were hesitant about receiving a regular SARS-CoV-2 vaccination. We have identified key factors associated with hesitancy for regular SARS-CoV-2 vaccination, which can be used to identify groups of HCWs at the highest risk of vaccine hesitancy and tailor interventions accordingly. Family and friends of HCWs may influence decisions about regular vaccination. This implies that working with HCWs and their social networks to allay concerns about SARS-CoV-2 vaccination could improve uptake in a regular vaccination programme. Trial registration ISRCTN Registry, ISRCTN11811602.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".