Documenting First Nations Access to COVID Vaccines: A whole-population linked administrative data study.
Bibliographic record
Abstract
ObjectivesFirst Nations (FN) organizations worked with public health and governments to improve FN access to COVID-19 vaccines by prioritizing FN communities in vaccination initiatives. FN researchers and data scientists partnered to test whether these efforts were associated with increased access to COVID-19 vaccines among FN compared with all other Manitobans. ApproachThis retrospective cohort study linked whole-population administrative data from (i) the First Nations research file, (ii) COVID testing and vaccination data, and (iii) health and social services for sociodemographic data and information on potential confounders. Several public health policies were created to improve access to COVID vaccines among FN; we tested whether FN received their 1st and 2nd vaccines sooner than all other Manitobans (AOM) using restricted mean survival time models. We adjusted for sociodemographic characteristics, comorbidities, and whether FN lived on- or off-reserve. We conducted sex-specific and effect modification analyses to test whether associations differed by sex. ResultsPrioritizing FN to receive vaccines was associated with increased vaccine uptake compared with AOM. After adjusting for various confounders, FN received their first dose 15.5 (95% CI 14.9 – 16.0) days sooner than AOM and their second dose 13.9 (13.3 – 14.5) days sooner than AOM. Sex-stratified and subsequent effect modification analyses using interaction terms, found that differences were greater for males than for females: FN males received their first dose 18.1 (17.3 – 18.8) days sooner than AOM males and FN females received their first dose 12.9 (12.2 – 13.7) days sooner than AOM females. This pattern held for second doses as well. FN with comorbidities also received vaccines sooner than AOM with similar comorbidity levels 20.9 days (23.1 – 18.8) among those with 3+ comorbidities. ConclusionPartnerships between public health entities and FN organizations that respect FN community sovereignty were instrumental in supporting FN health and well-being during COVID-19. Policies and programs that prioritized FN people for vaccines improved uptake saving lives. This partnership-based COVID-19 response can provide a framework for future public health efforts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".