COVID-19 Policy Decisions in Manitoba and the Experiences of the Red River Métis: A partnership-based, whole-population linked administrative data study
Bibliographic record
Abstract
Systematically marginalized populations, like Red River Métis, have been greatly affected by COVID-19. Manitoba’s Indigenous COVID-19 vaccine policy initially delayed prioritization of Métis. Our research team, which included Métis partners, examined the consequences of these decisions (COVID-19 infections, health service use, vaccine uptake) among Métis, and how earlier prioritization could have improved outcomes. This retrospective cohort study linked data from the Métis Population Database to whole-population COVID testing and vaccination data, and administrative data on health service use. Restricted mean survival time models tested whether vaccination uptake differed between Métis and all other Manitobans (AOM), adjusting for sociodemographic characteristics and comorbidities. A Bayesian model will simulate how prioritization of Métis for vaccination two weeks earlier could have impacted infections. Cumulative prevalence of COVID-19 infection rates were similar among Métis and AOM until May 2021 when rates became higher among Métis. Between May and December 2021, rates of first vaccination were lower among Metis than AOM, as were second vaccination rates between July and November 2021. Métis were more likely than AOM to be hospitalized due to Covid-19 between March and August 2021 and visit physicians for COVID related reasons from October 2020 to Feb 2021 and November 2021 to March 2022. Our analyses simulated what would have occurred had Métis been prioritized for vaccination two weeks before AOM, alongside other Indigenous peoples. Understanding the experiences of Métis relative to AOM is critical to identifying public health strategies which close gaps in vaccine uptake and infections.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".