Perceptions of COVID-19 vaccination and factors influencing COVID-19 vaccine acceptance among indigenous peoples in Quebec, Canada: Insights from a facebook posts and comments analysis
Bibliographic record
Abstract
Vaccination rates in Canada tend to be lower among Indigenous peoples than the rest of the population. The COVID-19 pandemic provided an unprecedented opportunity to better understand Indigenous perceptions about vaccination. The aim of this study was to explore perceptions of COVID-19 vaccine and other factors influencing COVID-19 vaccine acceptance as evidenced by public posts and comments on Facebook by Indigenous peoples in Quebec, Canada. We collected data on 95 Facebook pages or groups used by Indigenous peoples in Quebec between November 1, 2020, to June 15, 2021. To identify posts relating to COVID-19 vaccination, a keyword search ("vaccination," "vaccine," "shot," "does," "Moderna," "Pfizer") was carried out in English and French in the search bar of each Facebook page/group. Results show that First Nations peoples and Inuit in Quebec had important concerns about the usefulness, safety and effectiveness of COVID-19 vaccine. They also expressed fear of being used as test subjects for the rest of the population. Motivations mentioned by First Nations peoples and Inuit to get vaccinated against COVID-19 included to travel again and return to normal life with their loved ones, and the desire to protect the most vulnerable in their communities, especially Elders. Results show that Indigenous health care professionals were considered as reliable and trustful source of information regarding COVID-19, and that seeing role models being vaccinated build confidence and foster acceptance of the vaccine. Culturally adapted messages and vaccination campaigns by and for Indigenous peoples appear to be key to building trust toward COVID-19 vaccination.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".