The protective power of dissent? A longitudinal study on cognitive and socio-emotional determinants of COVID-19 vaccine hesitancy among young people in Canada
Bibliographic record
Abstract
COVID-19 has elicited polarized reactions to public health measures, fueling anti-vaccination movements worldwide which indicate that vaccine hesitancy represents a common expression of dissent. We investigate changes in cognitive (i.e., trust in government, conspiracy beliefs, vaccine attitudes, and other COVID-19-related factors) and socio-emotional factors (i.e., psychological distress and social support) over time, and examine if these factors are associated with COVID-19 vaccine hesitancy. A sample of Canadian young adults ( N = 2,695; 18 to 40 years old) responded to an online survey in May/June 2021 (after the first vaccination campaign) and then in November 2021 (after vaccine mandates were introduced). Based on survey answers, participants were categorized as “not hesitant”, “hesitant”, and “do not intend to get vaccinated” at each time point. Results from generalized estimating equation models indicate that vaccination hesitancy decreased over time. The importance attributed to specific COVID-19-related factors (e.g., research and science about COVID-19 vaccines, opinions of friends and family) decreased whereas psychological distress increased over time. Cognitive and socio-emotional factors were associated with vaccine hesitancy, with participants who did not intend to get vaccinated reporting the lowest psychological distress scores. We argue that dissent may be an empowering way for young people to restore a sense of personal agency via the opposition to a system perceived as illegitimate and/or unfair. These results raise important questions about potential collateral effects of top-down government and public health interventions in times of crisis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".