Impact of COVID-19 on vaccine confidence and uptake: A systematic literature review
Bibliographic record
Abstract
During the coronavirus disease 2019 (COVID-19) pandemic, scheduled vaccinations were postponed, mass vaccination programmes were suspended and opportunities for healthcare workers to administer vaccines ad hoc decreased. The aims of this systematic literature review were to determine the impact of the COVID-19 pandemic on vaccine confidence, intent and uptake in preexisting routine childhood or adult vaccination programmes, and to identify factors associated with changes in acceptance, intent and uptake of preexisting vaccines. Medline and Embase were searched for studies in Australia, Brazil, Canada, China, Japan, the USA, and European countries, published between 1 January 2021 and 4 August 2022. A complementary gray literature search was conducted between 11 and 13 October 2022, and supplemented with additional gray research in October 2023. In total, 54 citations were included in the review. Study design and geography were heterogeneous. The number of adults who received or intended to receive an influenza or pneumococcal vaccine was higher during the pandemic than in previous seasons (n = 28 studies). In addition, increased acceptance of adult vaccinations was observed during 2020–21 compared with 2019–20 (n = 12 studies). The rates of childhood vaccinations decreased during the COVID-19 pandemic across several countries (n = 11 studies). Factors associated with changes in intention to receive a vaccination, or uptake of influenza vaccine, included previous vaccination, older age, higher perceived risk of contracting COVID-19, anxiety regarding the pandemic and fear of contracting COVID-19. Acceptance and uptake of influenza and pneumococcal vaccines generally increased after onset of the COVID-19 pandemic.
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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 | 0.014 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".