How the population worldwide is reacting to the COVID-19 vaccines: a systematic review on hesitancy
Bibliographic record
Abstract
Abstract Background High rates of vaccination are worldwide required to establish a herd immunity stopping 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 was performed in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Multiple databases were searched, namely PubMed and Web of Science, on February 24th, 2021 using a set of developed keywords. Inclusion criteria included the study to be 1) conducted in English; 2) investigated attitudes, hesitancy, and/or barriers to COVID-19 vaccine acceptability among a given population; 3) utilized validated measurements techniques; 4) have the full text paper available and 5) be peer-reviewed prior to final publication. The Newcastle Ottawa (NOS) scale for cross sectional studies was used to assess the quality of the studies. Results 73 studies were included in qualitative synthesis. Overall, vaccine acceptance rates ranged from 23.6% in Kuwait to 94.3% in Malaysia and Nepal. A variety of different factors contributed to increased hesitancy, some of which included 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, less educated, with lower income, with no insurance, living in the rural area and self-identified as a racial/ethnic minority. Conclusions Vaccine hesitancy rates against COVID-19 vaccine ranged widely among across different populations. Identifying the factors that interplay and result in high hesitancy rates among a population can allow formulating a directed intervention to increase their vaccination uptake rates. Key messages It is necessary to understand the factors that contribute to the COVID-19 vaccine hesitancy. It is important to inform policy-makers and formulate direct intervention measures that will successfully handle the 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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".