Experiences of Red River Métis Accessing COVID Vaccines: A partnership-based, whole-population linked administrative data study.
Bibliographic record
Abstract
ObjectivesRed River Métis are Indigenous people hailing from the Canadian Prairies who have historically experienced poor health outcomes due to colonial practices. Researchers from the Manitoba Métis Federation (MMF) partnered with health services researchers to test whether MMF-led COVID initiatives were associated with access to COVID-19 testing and vaccines. ApproachWe linked the Métis Population Data-Base from the MMF (to identify Red River Métis) with whole-population COVID testing and vaccination data and health and social services administrative data (for information on sociodemographics and confounders) to complete this retrospective cohort study. We used restricted mean survival time models to test whether COVID-19 vaccination differed between Métis and all other Manitobans (AOM); models adjusted for demographics, comorbidities, and other characteristics (age, socioeconomic status, urbanicity, and mental health status). Data were stratified by sex and subsequent effect modification analyses tested whether associations differed by sex and physical health comorbidities. ResultsCOVID testing rates were lower during the first year of the pandemic among Métis than among AOM. During the second year of the pandemic, this finding was reversed - Métis accessed tests at higher rates. There was no difference between Métis and AOM in accessing first vaccine doses before implementation of MMF-led initiatives. After initiatives were put in place, Métis received their second COVID vaccine, on average, 1.3 (95% CI 1.9-0.6) days sooner than AOM, after adjusting for confounders. Effect modification analyses showed this relationship was concentrated among females – female Métis received their second vaccine 1.7 (2.6-0.8) days sooner than female AOM; differences were non-significant for males. Métis with 2+ comorbidities received their vaccine second 2.9 (5.3-0.5) days sooner than AOM with 2+ comorbidities. ConclusionPublic health initiatives prioritizing Métis for vaccines improved uptake. Initiatives led by Métis to improve COVID outcomes were critical to supporting Métis during the course of the pandemic. Public health response efforts need to operate from a standpoint that honours Indigenous sovereignty in their design and implementation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".