On different roles of interpersonal and institutional trust and trust in scientists in shaping COVID-19 vaccine hesitancy
Bibliographic record
Abstract
This study investigated the influence of interpersonal and institutional trust and trust in scientists on COVID-19 vaccine hesitancy. It hypothesized that institutional trust and trust in scientists are associated with reduced vaccine hesitancy, while interpersonal trust is not associated with vaccine hesitancy. These hypotheses were tested by estimating a set of multilevel regression models for a cross-sectional multi-country survey. The findings of the study confirmed all of the hypotheses mentioned above. The findings are relevant for subsamples of individuals who had (and did not have) COVID-19, lived (and did not live) with someone who had the virus, were satisfied (and not satisfied) with healthcare, and reported better (and worse health). The findings did not change with alternative operationalization of outcome and predictor variables and modification in the statistical specification. Remarkably, the magnitude of influence of institutional trust and trust in scientists is greater than that of GDP growth at the national level and net income at the individual level. These findings suggest promoting institutional trust and trust in scientists is an important way to reduce vaccine hesitancy and increase vaccine uptake.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".