Social cohesion predicts COVID‐19 vaccination intentions and uptake
Bibliographic record
Abstract
Abstract COVID‐19 vaccination is widely regarded as an individual decision, resting upon individual characteristics and demographic factors. In this research, we provide evidence that psychological group membership, and more precisely, social cohesion—a multidimensional concept that encompasses one's sense of connectedness to, and interrelations within, a group—can help us understand COVID‐19 vaccination intentions (Study 1) and uptake (Study 2). Study 1 is a repeated‐measures study with a representative sample of 3026 Australians. We found evidence that social cohesion can be conceptualised as a multidimensional structure; moreover, social cohesion at Wave 1 (early in the COVID‐19) predicted greater vaccination intention and lower perceived risk of vaccination at Wave 2 (4 months later). In Study 2 (a cross‐sectional study, N = 499), the multidimensional structure of social cohesion was associated with greater uptake of vaccine doses (in addition to willingness to receive further doses and perceived risk of the vaccine). These relations were found after controlling for a series of demographic (i.e., sex, age, income), health‐related factors (i.e., subjective health; perceived risk; having been diagnosed with COVID‐19), and individual differences (political orientation, social dominance orientation, individualism). These results demonstrate the need to go beyond individual factors when it comes to behaviours that protect groups, and particularly when examining COVID‐19 vaccination—one of the most important ways of slowing the spread of the virus.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".