Portrait of French-speaking minorities with respect to vaccination against COVID-19
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (COVID-19) vaccination campaign highlighted the requirement to better understand the needs of different populations. French-speaking minorities (FSMs) have greater difficulty accessing quality care in French, and this problem was exacerbated during the COVID-19 pandemic. Objective: The aim of this survey was to develop a descriptive portrait of the health needs of FSMs in relation to the COVID-19 vaccination campaign by describing their vaccination status, attitudes and beliefs compared with English-speaking majorities. Methods: A survey was conducted among eligible participants using convenience sampling. Data measurement includes a descriptive statistical comparison using analysis of the variance, univariate logistic regressions and a two-proportions z-test. Results: Of the 1,505 respondents (554 FSMs vs. 951 English speakers), the FSMs have an average age of 51.4 years and 89.2% are Canadian citizens. Vaccination of children was preponderant among English speakers (74.2% vs. 86.3%), including against COVID-19 (58.6% vs. 73.9%). A higher proportion of FSMs had gotten vaccinated in order to obtain a vaccine passport (39% vs. 29.3%). Among the unvaccinated, FSMs were more likely to question the efficacy of vaccines (60% vs. 36.4%). Canadian citizen FSMs with higher education could be divided in relation to the vaccine regimen. Conclusion: This survey revealed differences between FSMs and the English-speaking majority in their perceptions of vaccine efficacy, particularly vaccination of children, and a polarization of attitudes/beliefs among FSMs according to certain sociodemographic factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".