A Descriptive Analysis of the Relationship between Social Media Use and Vaccine Hesitancy among a Sample of Unvaccinated Adults in Canada
Bibliographic record
Abstract
Both traditional and social media information sources have disseminated information on the COVID-19 pandemic. The content shared may influence public opinion on different mitigation strategies, including vaccination. Misinformation can alter risk perception and increase vaccine hesitancy. This study aimed to explore the impact of using social media as the primary information source about the COVID-19 vaccine on COVID-19 vaccine hesitancy among people living in Canada. Secondary objectives identified other predictors of vaccine hesitancy and distinguished the effects of using traditional and social media sources. We used quota sampling of adults in Canada [N = 985] to conduct an online survey on the Pollfish survey platform between 21st and 28th May 2021. We then used bivariate chi-squared tests and multivariable logistic regression modeling to explore the associations between using social media as one's primary source of information about the COVID-19 vaccine and vaccine hesitancy. We further analyzed the association between specific types of channels of information and vaccine hesitancy. After controlling for covariates such as age, sex, race, and ethnicity, individuals reporting social media as their primary source of COVID-19 vaccine information versus those who had not used social media as their primary source of COVID-19 vaccine information had 50% higher odds of vaccine hesitancy. Among different channels of information, we found that information from television was associated with a 40% lower odds ratio for vaccine hesitancy. Since social media platforms play an essential role in influencing hesitancy in taking the COVID-19 vaccination, it is necessary to improve the quality of social media information sources and raise people's trust in information. Meanwhile, traditional media channels, such as television, are still crucial for promoting vaccination programs.
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How this classification was reachedexpand
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".