A Systematic Review on Vaccine Hesitancy in Black Communities in Canada: Critical Issues and Research Failures
Bibliographic record
Abstract
Black communities have been disproportionately impacted by Coronavirus Disease 2019 (COVID-19) in Canada, in terms of both number of infections and mortality rates. Yet, according to early studies, vaccine hesitancy appears to be higher in Black communities. The purpose of this systematic review is to examine the prevalence and factors associated with vaccine hesitancy in Black communities in Canada. Peer-reviewed studies published from 11 March 2020 to 26 July 2022, were searched through eleven databases: APA PsycInfo (Ovid), Cairn.info, Canadian Business & Current Affairs (ProQuest), CPI.Q (Gale OneFile), Cochrane CENTRAL (Ovid), Embase (Ovid), Érudit, Global Health (EBSCOhost), MEDLINE (Ovid), and Web of Science (Clarivate). Eligible studies were published in French or English and had empirical data on the prevalence or factors associated with vaccine hesitancy in samples or subsamples of Black people. Only five studies contained empirical data on vaccine hesitancy in Black individuals and were eligible for inclusion in this systematic review. Black individuals represented 1.18% (n = 247) of all included study samples (n = 20,919). Two of the five studies found that Black individuals were more hesitant to be vaccinated against COVID-19 compared to White individuals, whereas the other three found no significant differences. The studies failed to provide any evidence of factors associated with vaccine hesitancy in Black communities. Despite national concerns about vaccine hesitancy in Black communities, a color-blind approach is still predominant in Canadian health research. Of about 40 studies containing empirical data on vaccine hesitancy in Canada, only five contained data on Black communities. None analyzed factors associated with vaccine hesitancy in Black communities. Policies and strategies to strengthen health research in Black communities and eliminate the color-blind approach are discussed.
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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 | 0.042 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".