How Do the Components of Social Capital Reduce COVID‐19 Vaccine Hesitancy? Lessons From a Canadian National Survey
Why this work is in the frame
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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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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it