Addressing Indigenous health determinants exacerbated by the COVID-19 pandemic
Bibliographic record
Abstract
Ongoing evidence-based reporting throughout the COVID-19 pandemic has uncovered health disparities arising from social determinants of health (SDOH) among Indigenous communities across Canada (Government of Canada, 2020).These SDOH-related disparities in health status are closely related to health inequities.For clarity, health disparity refers to differences in health across population groups, while health inequity refers to the causes of these health disparities (Reutter & Kushner, 2010).Through intersectoral collaboration, community health leaders can work with Indigenous communities to address these SDOH-related health disparities and inequities.It is well documented that Indigenous communities are at a much greater risk of poor health outcomes than non-Indigenous Canadians (Reutter & Kushner, 2010).Death rates, hospitalizations, and infectious transmission rates in both the 2009 H1N1 and the 1918 influenza pandemics were higher among Indigenous peoples than the national average (Saint-Girons et al., 2020).Similarly, emerging data from the current COVID-19 pandemic is revealing higher infection rates, which points to pre-existing health disparities and inequities among this population.Here, we identify current determinants of health of particular importance to poor Indigenous health outcomes during the COVID-19 pandemic in Canada, and some crucial ways in which community health leaders can address these determinants.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".