Gender differences in institutional trust and covid-19 vaccines uptake in Ghana
Bibliographic record
Abstract
One of the most studied social phenomena since the emergence of the covid-19 pandemic is trust. This renewed interest demonstrates the crucial role of trust in crises management. Unfortunately, most of the studies on trust have been conducted without considerations to saliant social categorisations like gender that might matter on how trust is built and radiated. This is despite differing socialisations of gender values in social contexts and how that might translate into how trust is built and demonstrated. The goal of this study, therefore, was to examine prevailing gendered patterns of institutional trust and how that might translate into the uptake of covid-19 vaccines in Ghana. We conducted logistic regression analyses on a sample of 1692 individual responses from a cross-sectional survey that was collected between October and November 2022 across four cities in Ghana. Results show that women were more trusting and were consequently more likely to take the vaccine in comparison to men. Interaction analysis further revealed that even when both men and women trusted a lot in relevant institutions like health, women were about 64% more likely to take the vaccine in comparison to men. Only men were significantly less likely to take the vaccine if they had trust concerns with political institutions. But both men and women were less likely to take the vaccine if they had trust concerns with health institutions. This study underscores the need for gender-specific policy programmes on public health issues.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".