Attitudes, acceptance and hesitancy among the general population worldwide to receive the COVID-19 vaccines and their contributing factors: A systematic review
Bibliographic record
Abstract
BACKGROUND: High rates of vaccination worldwide are required to establish a herd immunity and stop the current COVID-19 pandemic evolution. Vaccine hesitancy is a major barrier in achieving herd immunity across different populations. This study sought to conduct a systematic review of the current literature regarding attitudes and hesitancy to receiving COVID-19 vaccination worldwide. METHODS: A systematic literature search of PubMed and Web of Science was performed on July 5th, 2021, using developed keywords. Inclusion criteria required the study to (1) be conducted in English; (2) investigate attitudes, hesitancy, and/or barriers to COVID-19 vaccine acceptability among a given population; (3) utilize validated measurement techniques; (4) have the full text paper available and be peer-reviewed prior to final publication. FINDINGS: Following PRISMA guidelines, 209 studies were included. The Newcastle Ottawa (NOS) scale for cross-sectional studies was used to assess the quality of the studies.Overall, vaccine acceptance rates ranged considerably between countries and between different time points, with Arabian countries showing the highest hesitancy rates compared with other parts of the world. INTERPRETATION: A variety of different factors contributed to increased hesitancy, including having negative perception of vaccine efficacy, safety, convenience, and price. Some of the consistent socio-demographic groups that were identified to be associated with increased hesitancy included: women, younger participants, and people who were less educated, had lower income, had no insurance, living in a rural area, and self-identified as a racial/ethnic minority.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".