Confidence and Hesitancy During the Early Roll-out of COVID-19 Vaccines Among Black, Hispanic, and Undocumented Immigrant Communities: a Review
Bibliographic record
Abstract
Black and Hispanic Americans have been hardest hit with COVID-19 infections, hospitalizations, and deaths, yet during the first several months of vaccine roll-out they had the lowest level of vaccine uptake. Primarily, our research on vaccine hesitancy focused on skepticism around the vaccine itself and its roll-out. Our search strategy used PUBMED and Google with a prescribed set of definitions and search terms for two reasons: there were limited peer-reviewed studies during early period of roll-out and real-time perspectives were crucially needed. Literature searches occurred in April 2021and covered September 2020-April 2021. Analyses included expert opinion, survey results and qualitative summaries. Overall, for the general U.S. population, there was considerable hesitancy initially that remained high during the early roll-out. The general population expressed concerns over the speed of vaccine development ("warp speed"), confidence in the competence of government being involved in the development of vaccines and general mistrust of government. Among Black and Hispanic Americans, hesitancy was further expressed as mistrust in the medical establishment that was related to past and current medical mistreatment. Undocumented immigrants worried about access to insurance and possible deportation. These results on confidence in the vaccine early during vaccine roll-out suggest diverse reasons that influence a person's decision to vaccinate or not. Additional barriers to vaccine uptake include complacency and access. To ensure health equity, particularly to address disparities in morbidity and mortality, vaccine hesitancy needs to be acknowledged and addressed as COVID-19 vaccine roll-out continues, and these observations calls for conscious planning to address these issues early with future health crises.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".