How Do the Components of Social Capital Reduce COVID‐19 Vaccine Hesitancy? Lessons From a Canadian National Survey
Bibliographic record
Abstract
ABSTRACT This paper theorizes that not all components of social capital reduce vaccine hesitancy. Specifically, it hypothesizes that institutional trust, trust in experts, and social networks reduce vaccine hesitancy, while generalized trust and civic participation do not influence vaccine hesitancy. These hypotheses are tested using a large Canadian survey during the COVID‐19 pandemic. The data originate from the publicly available national survey of the Canadian general population aged 18 and older ( N = 9829). Binomial logistic regression is estimated to establish the influence of social capital components on vaccine hesitancy while controlling for a comprehensive set of covariates, including the socio‐demographics of the respondents, their political views, media exposure, self‐reported health status, and province of residence. The odds ratios, significance levels, and 95% confidence intervals are reported. The results confirmed the posted hypotheses by suggesting that institutional trust has the strongest influence on reducing vaccine hesitancy, followed by the influence of trust in experts and the size of the social networks. Conversely, the influence of generalized trust and civic participation on vaccine hesitancy was not statistically significant. The findings of this paper suggest that an increase in institutional trust, effectively using experts' opinions, and taking into account features of social networks will increase vaccination uptake and reduce 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".