Vaccine hesitancy and respect for public health measures: Citizens’ trust in politicians and public servants across national, subnational and municipal levels of government
Bibliographic record
Abstract
Research shows that citizens' trust in government is associated with lower vaccine hesitancy and an increased willingness to follow public health measures. Thus far, however, the population health literature has largely conceptualized "government" as a unitary actor. This article furthers our understanding of this relationship by examining two important features of modern governance that have largely gone unexamined: (1) that governing involves popularly elected politicians and appointed bureaucrats; and (2), that governing often comprises many levels of government within the same country. Analyzing survey data from Canada with various multivariate regression models, this article finds that the relationship political trust has with vaccine hesitancy and intention to follow for public health measures is more complex than presently recognized. Specifically, a larger change in citizens' public health behaviors is associated with trust in public health officials than with trust in government, and of particular importance is trust in national public health authorities, despite the fact that public health measures in Canada are largely the jurisdiction of subnational governments. The implications of these findings for population health research and policymakers are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".