Intragroup differences in COVID-19 vaccine attitudes among Black Americans
Bibliographic record
Abstract
COVID-19 vaccine hesitancy among Black Americans threatens to further magnify racial inequities in COVID-19 related health outcomes that emerged in the earliest stages of the pandemic. Here we shed new light on attitudes towards COVID-19 vaccines by considering intragroup variation. Rather than analyzing Blacks as a homogenous group, we examine the relationship between COVID-19 vaccine attitudes and the extent to which participants are aligned with African American versus White culture (i.e., level of “acculturation”). In a sample of N=997 Black Americans, we find that stronger alignment with African American culture predicts substantially more negative attitudes towards COVID-19 vaccination, above and beyond variation explained by age, gender, education, and socioeconomic status. This relationship was substantially attenuated when controlling for suspicion of the healthcare system, but not perceptions that healthcare system treats Blacks unfairly, science knowledge, or cognitive reflection. The intragroup differences among Blacks in COVID-19 vaccine perceptions uncovered here provide insights into designing interventions that provide health information that targets the relevant factors for vaccine hesitancy in differing subgroups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".