COVID-19 vaccine hesitancy and acceptance: a comprehensive scoping review of global literature
Bibliographic record
Abstract
As countries continue the third year of the pandemic, we believe that there has been unfair attention to COVID-19 vaccine efficacy and safety, while tacitly ignoring serious challenges with vaccine uptake, without which vaccination may not be effective against the spread of COVID-19. While several studies have been published on COVID-19 vaccine hesitancy, there remains a need to conduct a comprehensive global analysis of vaccine hesitancy. We conducted a scoping review of 60 studies published globally on vaccine hesitancy and acceptance. We conducted a qualitative analysis to identify motivators and barriers to vaccination across several cultural and demographic contexts. We found the following factors to be relevant in any discussion about addressing or minimizing vaccine hesitancy: risk perceptions, trust in health care systems, solidarity, previous experiences with vaccines, misinformation, concerns about vaccine side effects and political ideology. We combine our insights from this comprehensive review of global literature to offer an important and practical discussion about two strategies that have been used to improve vaccine uptake: (i) communication and education and (ii) vaccine rollout and logistics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 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".