Understanding COVID-19 vaccination disparity among Black adults in North America: A two-study motivational approach
Bibliographic record
Abstract
Previous research has revealed lower vaccination rates among Black communities relative to other racial-ethnic communities in North America. However, there remains a gap in understanding the motivational barriers contributing to these persistent disparities. Grounded in Self-Determination Theory, the current research aimed to examine the effects of different motivations for vaccine acceptance across population groups (autonomous, controlled, and amotivation). The current investigation involved two survey studies conducted in the United States and Canada during the second year of the COVID-19 pandemic. Study 1 was cross-sectional and included 623 Americans (60.4 % female). Study 2 was a prospective longitudinal study of 413 Canadians (54 % female; M age = 47.6, SD = 17.9). In both studies, Black adults reported significantly lower levels of vaccination (M Black = 1.15 vs. M Non-Black = 1.48 in Study 1; M Black = 2.25 vs. M Non-Black = 2.63 in Study 2), lower autonomous motivation, and higher distrust-based amotivation compared to individuals from other population groups. In the cross-sectional study, autonomous motivation (β = 0.45, p < .001) was positively associated with vaccine uptake while distrust-based amotivation (β = −0.23, p < .001) was negatively associated with vaccine uptake. In the longitudinal study, distrust-based amotivation (β = −0.11, p < .01) was associated with vaccination uptake for all groups, while lower autonomous motivation ( b = 0.17, p < .01) and higher controlled motivation ( b = −0.14, p < .05) were associated with lower vaccine uptake among Black individuals. These findings suggest that while addressing distrust-based amotivation at the institutional and systemic level to promote utilization of vaccination services is essential across all population groups, tailored public health interventions and policies that foster a sense of autonomy over one's healthcare decisions may play a particularly significant role for Black adults in supporting vaccine acceptance and 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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".