Institutional trust, conspiracy beliefs and Covid-19 vaccine uptake and hesitancy among adults in Ghana
Bibliographic record
Abstract
Vaccine hesitancy is considered one of the ten threats to global health. In the context of the COVID-19 pandemic, vaccine hesitancy may undermine efforts toward controlling or preventing the disease. Nevertheless, limited research has examined vaccine hesitance, particularly in low- and middle-income countries (LMICs). It is thus imperative to examine how institutional trust and conspiracy belief in tandem influence the uptake of COVID-19 vaccines. Using data (n = 2059) from a cross-sectional study in Ghana, this study examines the association between institutional trust, conspiracy beliefs, and vaccine uptake among adults in Ghana using logistics regression. The regression model (model 3) adjusted for variables such as marital status, age, gender, employment, income, and political affiliations. The results show that individuals were significantly less likely to be vaccinated if they did not trust institutions (OR = .421, CI = .232-.531). Similarly, we found that individuals who believed in conspiracy theories surrounding the COVID-19 vaccine were less likely to be vaccinated (OR = .734, CI = .436-.867). We also found that not having a COVID-19-related symptom is associated with vaccine refusal (OR = .069, CI = .008-.618). Similarly, compared to those with a vaccine history, those without a vaccine history are less likely to accept the COVID-19 vaccine (OR = .286, CI = .108-.756). In conclusion, our results demonstrate the need for enhanced education to tackle conspiracy beliefs about the disease and enhance vaccine uptake. Given the role of trust in effecting attitudinal change, building trust and credibility among the institutions responsible for vaccinations ought to be prioritized.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".