COVID-19 vaccine acceptance and hesitancy in Ghana: A systematic review
Bibliographic record
Abstract
The propensity to accept vaccines and factors that affect vaccine acceptance and hesitancy will determine the overall success of the COVID-19 vaccination program. Therefore, countries need to understand the factors that influence vaccine acceptance and hesitancy to prevent further future shocks, and it is necessary to have a thorough understanding of these factors. As a result, this study aims to review selected published works in the study's domain and conduct valuable analysis to determine the most influential factors in COVID-19 vaccine acceptance and hesitancy in Ghana. The review also explored the acceptance rate of COVID-19 vaccines in Ghana. We selected published works from 2021 to April 2023 and extracted, analyzed, and summarized the findings based on the key factors that influence COVID-19 vaccine acceptance and hesitancy in Ghana, the acceptance rate in Ghana, the demographic factors that are often examined, and the study approach used to examine these factors. The study found that positive vaccination perception, safety, belief in vaccine efficacy, knowledge of COVID-19, and a good vaccine attitude influence COVID-19 vaccine acceptance in Ghana. The negative side effects of the vaccines, mistrust in the vaccine, lack of confidence in the vaccine's safety, fear, and spiritual and religious beliefs all played significant roles in influencing COVID-19 vaccine hesitancy. For this study, the COVID-19 acceptance rates observed in the reviewed articles ranged from 17.5% to 82.6%. The demographic parameters frequently included in these studies that have a significant impact include educational attainment, gender, religious affiliation, age, and marital status. The positive perceptions of the COVID-19 vaccine and concerns about its negative effects influenced Ghanaians' acceptance and hesitancy.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".