Social network risk factors and COVID-19 vaccination: A cross-sectional survey study
Bibliographic record
Abstract
BACKGROUND: Social networks have an important impact on our health behaviours, including vaccination. People's vaccination beliefs tend to mirror those of their social network. As social networks are homogenous in many ways, we sought to determine in the context of COVID-19 which factors were most predictive of belonging to a mostly vaccinated or unvaccinated social group. METHODS: We conducted a cross-sectional survey among Canadian residents in November and December 2021. Participants were asked about the vaccination status of their social networks their beliefs relating to COVID-19, and various sociodemographic factors. Respondents were split into three groups based on social network vaccination: low-, medium-, and high-risk. Chi-squared tests tested associations between factors and risk groups, and an ordinal logistic model was created to determine their direction and strength. RESULTS: Most respondents (81.1 %) were classified as low risk (i.e., a mostly vaccinated social network) and few respondents (3.7 %) were classified as high-risk (i.e., an unvaccinated social group). Both the chi-square test (29.2 % difference between the low- and high- risk groups [1.8 % vs. 31.0 %], p < 0.001) and the ordinal logistic model (odds ratio between the low- and high-risk groups: 14.45, p < 0.01) found that respondents' perceptions of COVID-19 as a "not at all serious" risk to Canadians was the most powerful predictor of belonging to a predominantly unvaccinated social circle. The model also found that those in mostly unvaccinated social circles also more often reported severe COVID-19 symptoms (odds ratio between the low- and high-risk groups: 2.26, p < 0.05). CONCLUSION: Perception of COVID-19 as a threat to others may signal communities with lower vaccination coverage and higher risk of severe outcomes. This may have implications for strategies to improve public outreach, messaging, and planning for downstream consequences of low intervention 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".