COVID-19 vaccine hesitancy in an ethnically diverse community: descriptive findings from the Born in Bradford study
Bibliographic record
Abstract
Background : The roll out of coronavirus disease 2019 (COVID-19) vaccines are now underway in the UK, and ensuring good uptake in vulnerable communities will be critical to reducing hospital admissions and deaths. There is emerging evidence that vaccine hesitancy is higher in ethnic minorities and deprived areas, and that this may be caused by misinformation in the community. This study aims to understand COVID-19 vaccine hesitancy in an ethnically diverse and deprived population. Methods : Questionnaire surveys were sent to parents in the Born in Bradford study. Cross tabulations explored variation by ethnicity and deprivation. Text from open-ended questions was analysed using thematic analysis. Results : 535 (31%) of 1727 invited between 29 th October-9 th December 2020 participated in the study. 154 (29%) of respondents do want a vaccine, 53 (10%) do not. The majority had not thought about it (N=154, 29%) or were unsure (N=161, 30%). Vaccine hesitancy differed significantly by ethnicity and deprivation: 43% (95% CIs: 37-54%) of White British and 60% (35-81%) in the least deprived areas do want a vaccine, compared to 13% (9-19%) of Pakistani heritage and 20% (15-26%) in the most deprived areas. Those that distrusted the NHS were more likely to not want a vaccine (30%, 15-50%). Reasons for not wanting a vaccine were commonly explained by confusion and distrust caused by prevalent misinformation. Conclusions : There is a much higher level of vaccine hesitancy in ethnic minorities, those living in deprived areas and those that distrust the NHS. There is an urgent need to tackle the overwhelming misinformation about COVID-19 that is leading to this uncertainty and confusion about the vaccines. If not addressed there is a high risk of unequitable roll out of the vaccination programme in the UK.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".