Barriers and Concerns that Contribute to Vaccine Hesitancy in Black, Indigenous, and People of Colour (BIPOC) Individuals in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Despite research demonstrating the effectiveness of COVID-19 vaccines, hesitancy is extremely common in minority communities. The purpose of this study was to identify key barriers and concerns that contribute to vaccine hesitancy in Black, Indigenous, and People of Colour (BIPOC) individuals and provide recommendations to address these barriers and concerns. METHODS: The study was an online cross-sectional survey conducted among 1491 BIPOC and Caucasian adults, recruited using social media networks in August-September 2021. The questionnaire consisted of five sections that probed concerns and attitudes contributing to COVID-19 vaccine hesitancy. RESULTS: Respondents were mostly Caucasian males (75.7%) and the average age was 29.1 years. A higher proportion of BIPOC respondents received both doses (50.6%) than Caucasian respondents (36.4%). Out of the unvaccinated, a higher percentage of BIPOC respondents did not plan on getting vaccinated (17.1%) compared to Caucasian respondents (4.2%). BIPOC respondents preferred the Pfizer-BioNTech (34.1%) vaccine whereas Caucasian respondents preferred AstraZeneca (29.3%). The biggest concern BIPOC and Caucasian respondents had with COVID-19 vaccines were side effects (56.6% vs 54.4%, respectively). BIPOC respondents identified dependability as the next biggest concern after side effects. A higher percentage of BIPOC respondents were against getting vaccinated against COVID-19 (16% vs 1.2%) compared to Caucasian respondents. CONCLUSION: Among unvaccinated respondents, COVID-19 vaccine hesitancy was most evident in the BIPOC respondents compared to Caucasian respondents. Side effects, trustworthiness, and lack of information were identified as the three most common concerns surrounding vaccines in general. Increased accessibility to reliable and accurate vaccine information in various languages/dialects can raise awareness about COVID-19 vaccinations in BIPOC communities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".