The relationship between higher rates of COVID-19 and infrastructure on First Nations Reserves in Manitoba
Bibliographic record
Abstract
In this study, I conducted an ecological analysis at the community level to examine the association between COVID-19 case rates and socioeconomic with infrastructure characteristics in 22 First Nations and 27 non-First Nations communities in Manitoba. I analyzed the association between COVID-19 case rates and socioeconomic variables using linear (bivariate and multivariate) regression and spatial analyses. The data on COVID-19 rates up to June 01, 2021, was obtained from the Government of Manitoba public COVID-19 data portal. The infrastructure and socioeconomic data were obtained from publicly available datasets, including the 2021 Statistics Canada population census database, the Government of Canada database on Indigenous peoples and communities, Government of Manitoba Regional Health Authorities. Information on the geographical coordinates of the communities was from the Canadian Geographical Names Database (CGNDB). The simple linear regression showed COVID-19 case rates in Manitoba were significantly associated with the community rates for (a) unsuitable housing (standardized regression coefficient [β] = 0.65, coefficient of determination [R2]= 0.42, p < 0.05), (b) average household size (β = 0.60, R2 = 0.36, p < 0.05), (c) major repairs in housing needed (β = 0.55, R2 = 0.30, p < 0.05), (d) access to a service centre (β = - 0.45, R2 = 0.21, p < 0.05), (e) proximity to a hospital (β = - 0.56, R2 = 0.31, p < 0.05), (f) median after-tax income (β = - 0.50, R2 = 0.25, p < 0.05), and (g) college degree or higher (β = - 0.47, R2 = 0.22, p < 0.05). There was no significant association between COVID-19 rates and high school degree ((β = - 0.45, R2 = 0.21, not significant). Unsuitable housing was the only statistically significant variable in the multivariate regression (β = 1.59, p < 0.05), and the multivariate model accounted for 58% of the variance observed in the COVID-19 rates. The maps showed that First Nations in northern Manitoba suffered the most from high COVID-19 rates. Pandemic interventions and post-pandemic policies should ensure every community has adequate standard housing, equal access to hospitals, basic income, educational opportunities and road access, particularly in First Nations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".