Contextualizing Vaccine Hesitancy: A Scoping Review of Factors Influencing COVID-19 Vaccine Uptake
Bibliographic record
Abstract
Background: The development of COVID-19 vaccines is crucial in the fight against the pandemic; however, vaccine hesitancy was a growing concern amplified by the rapid development of COVID-19 vaccines. This review aims to explore the underlying factors influencing vaccine acceptance and hesitancy, including socio-demographic characteristics and health beliefs. Methods: We conducted a scoping review to examine literature and major factors impacting people's willingness to take COVID-19 vaccines. A literature search was performed using four major literature databases: Medline®, Embase®, CINAHL®, and Scopus®. A total of 30 articles fit the predetermined criteria for this sample search. The articles were independently screened to identify the study location, sampling method, study design, and enablers and barriers to vaccination. Results: Studies were included from five different continents and the findings indicating the following six main areas had significant impact on COVID-19 vaccine acceptance: (1) vaccine safety and efficacy, (2) trust in government and political views, (3) COVID-19 risk perception, (4) cultural factors, (5) knowledge about COVID-19 and public health messaging, and (6) income level and vaccine cost. Various studies had conflicting results highlighting the influence of environmental factors and the need for unique and targeted public health interventions. Conclusion: Identifying and understanding factors that affect vaccine uptake can aid in the development of effective strategies to improve public health. Our findings suggest that additional efforts should be made by healthcare personnel and public health officials in terms of educating the public and understanding the influence of environmental and personal belief factors. Financial barriers should also be carefully considered to overcome accessibility issues in countries where healthcare is not funded by the government.
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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 | 0.023 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 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".