Modeling the Impact of the COVID-19 Pandemic on First Nations, Metis, and Inuit Communities: Some Considerations
Bibliographic record
Abstract
Objectives: This article articulates the complexity of modeling in First Nations, Metis, and Inuit contexts by providing the results of a modeling exercise completed at the request of the First Nations Health and Social Secretariat of Manitoba. Methods: We developed a model using the impact of a previous pandemic (the 2009 H1N1) to generate estimates. Results: The lack of readily available data has resulted in a model that assumes homogeneity of communities in terms of health status, behaviour, and infrastructure limitations. While homogeneity may be a reasonable assumption for province-wide planning, First Nation communities and Tribal Councils require more precise information in order to plan effectively. Metis and urban Inuit communities, in contrast, have access to much less information, making the role of Indigenous organizations mandated to serve the needs of these populations that much more difficult. Conclusion: For many years, Indigenous organizations have advocated for the need to have access to current and precise data to meet their needs. The COVID-19 pandemic demonstrates the importance of timely and accurate community-based data to support pandemic responses.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".