What factors promote vaccine hesitancy or acceptance during pandemics? A systematic review and thematic analysis
Bibliographic record
Abstract
Examine the factors that promote vaccine hesitancy or acceptance during pandemics, major epidemics and global outbreaks. A systematic review and thematic analysis of 28 studies on the Influenza A/H1N1 pandemic and the global spread of Ebola Virus Disease. We found seven major factors that promote vaccine hesitancy or acceptance: demographic factors influencing vaccination (ethnicity, age, sex, pregnancy, education, and employment), accessibility and cost, personal responsibility and risk perceptions, precautionary measures taken based on the decision to vaccinate, trust in health authorities and vaccines, the safety and efficacy of a new vaccine, and lack of information or vaccine misinformation. An understanding of participant experiences and perspectives toward vaccines from previous pandemics will greatly inform the development of strategies to address the present situation with the COVID-19 pandemic. We discuss the impact vaccine hesitancy might have for the introduction and effectiveness of a potential COVID-19 vaccine. In particular, we believe that skepticism toward vaccines can still exist when there are no vaccines available, which is contrary to contemporary conceptualizations of vaccine hesitancy. We recommend conducting further research assessing the relationship between the accessibility and cost of vaccines, and vaccine hesitancy.
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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 | 0.019 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".