Why some people do not get vaccinated against COVID‐19: Social‐cognitive determinants of vaccination behavior
Bibliographic record
Abstract
It is puzzling that a sizeable percentage of people refuse to get vaccinated against COVID-19. This study aimed to examine social psychological factors influencing their vaccine hesitancy. This longitudinal study traced a cohort of 2663 individuals in 25 countries from the time before COVID-19 vaccines became available (March 2020) to July 2021, when vaccination was widely available. Multilevel logistic regressions were used to examine determinants of actual COVID-19 vaccination behavior by July 2021, with country-level intercept as random effect. Of the 2663 participants, 2186 (82.1%) had been vaccinated by July 2021. Participants' attitude toward COVID-19 vaccines was the strongest predictor of both vaccination intention and subsequent vaccination behavior (p < .001). Perceived risk of getting infected and perceived personal disturbance of infection were also associated with higher likelihood of getting vaccinated (p < .001). However, religiosity, right-wing political orientation, conspiracy beliefs, and low trust in government regarding COVID-19 were negative predictors of vaccination intention and behavior (p < .05). Our findings highlight the importance of attitude toward COVID-19 vaccines and also suggest that certain life-long held convictions that predate the pandemic make people distrustful of their government and likely to accept conspiracy beliefs and therefore less likely to adopt the vaccination behavior.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".