Understanding Factors that Contributes to Vaccine Hesitancy in the COVID-19 Context: The Intersection of Trust in Institutions, Socioeconomic Status and Demographic Factors
Bibliographic record
Abstract
Vaccine hesitancy has become a major global public health challenge, threatening the success of vaccination programs. Despite the fact that vaccines have proven effective in fighting infectious diseases, millions of people are still hesitant to get vaccinated, especially during the pandemic. This paper identifies vaccine hesitancy in the COVID era and examines the factors that contribute to vaccine hesitancy, including a lack of trust in health institutions, misinformation, and other cognitive and demographic factors. Drawing on examples from around the world, including Africa and Brazil, this article further explores the social and political determinants that influence vaccine uptake. While Brazil has shown relatively high vaccine acceptance rates due to trust in local vaccines and the urgency of the pandemic, Africa faces great hesitation due to mistrust and misinformation. By addressing misinformation, restoring public trust and confidence in vaccines, and involving communities in the decision-making process, public health initiatives can effectively combat vaccine hesitancy and mitigate ongoing threats to global health
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".