Multilevel determinants of Covid-19 vaccine hesitancy and undervaccination among marginalized populations in the United States: A scoping review
Bibliographic record
Abstract
Abstract Background Amid persistent disparities in Covid-19 vaccination, we conducted a scoping review to identify multilevel determinants of Covid-19 vaccine hesitancy (VH) and undervaccination among marginalized populations in the U.S. Methods We utilized the scoping review methodology developed by the Joanna Briggs Institute and report all findings according to PRISMA-ScR guidelines. We developed a search string and explored 7 databases to identify peer-reviewed articles published from January 1, 2020–October 31, 2021, the initial period of U.S. Covid-19 vaccine avails.comability. We combine frequency analysis and narrative synthesis to describe factors influencing Covid-19 vaccination among marginalized populations. Results The search captured 2,496 non-duplicated records, which were scoped to 50 peer-reviewed articles: 11 (22%) focused on African American/Black people, 9 (18%) people with disabilities, 4 (8%) justice-involved people, and 2 (4%) each on Latinx, people living with HIV/AIDS, people who use drugs, and LGBTQ+ people. Forty-four articles identified structural factors, 36 social/community, 27 individual, and 40 vaccine-specific factors. Structural factors comprised medical mistrust (of healthcare systems, government public health) and access barriers due to unemployment, unstable housing, lack of transportation, no/low paid sick days, low internet/digital technology access, and lack of culturally and linguistically appropriate information. Social/community factors including trust in a personal healthcare provider (HCP), altruism, family influence, and social proofing mitigated VH. At the individual level, low perceived Covid-19 threat and negative vaccine attitudes were associated with VH. Discussion This review indicates the importance of identifying and disaggregating structural factors underlying Covid-19 undervaccination among marginalized populations, both cross-cutting and population-specific—including multiple logistical and economic barriers in access, and systemic mistrust of healthcare systems and government public health—from individual and social/community factors, including trust in personal HCPs/clinics as reliable sources of vaccine information, altruistic motivations, and family influence, to effectively address individual decisional conflict underlying VH as well as broader determinants of undervaccination.
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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 | 0.018 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".