Understanding COVID-19 Vaccine Hesitancy in Black, East Asian, and Eastern European Diasporic Communities in Toronto: A Scoping Review
Bibliographic record
Abstract
Canada achieved COVID-19 vaccination coverage of 83.2% in the total population (at least one dose). However, only 49.6% of Canadians completed the primary series plus one booster (which defines one as fully vaccinated). Inconsistent uptake of COVID-19 vaccines impeded pandemic response and led to increased demands in a stretched health care system. To advance pandemic preparedness, a critical understanding of vaccine access and hesitancy is needed. We undertook a scoping review to identify the primary reasons for vaccine hesitancy in Toronto's East Asian, Black, and Eastern European diaspora. A total of 5548 articles were retrieved from PubMed, OVID, JSTOR, ERIC and 27 and 43 from Google Scholar and Google respectively. De-duplication left us with 42 relevant sources for data extraction, including 19 news articles, 9 commentaries, 11 pieces of grey literature and 3 peer reviewed articles that were not identified via academic databases. Our review results revealed four factors for COVID-19 vaccine hesitancy among East Asian, Black, and Eastern European diasporas in Toronto: ( a ) access barriers; ( b ) mistrust; ( c ) racism; and ( d ) misinformation. These factors can create conditions of re-racialization by stereotyping entire ethnoracial groups or convincing members of these groups to become vaccine skeptics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".