First Nations Response to Covid‐19 Pandemic
Bibliographic record
Abstract
Stanley et al. describe the successes of the Aboriginal and Torres Strait Islander (TSI) communities as global exemplars of controlling the COVID-19 pandemic.1 Despite challenges similar to those facing Indigenous populations world-wide, the authors conclude that the remarkably low morbidity and mortality in these communities due to COVID-19 can be attributed to rapid implementation of best practices and Indigenous leadership alongside transparent communication and collaboration with governmental partners.1, 2 Unfortunately, American Indian/Alaska Native (AI/AN) communities in the United States have suffered disproportionately from COVID-19. According to the Centers for Disease Control, AI/AN people were three to five times more likely to be diagnosed with COVID-19, with twice the mortality rate of non-Hispanic white people.3 This demonstrates the impact of suboptimal health-care delivery due to decades of severe social inequities and limited access to resources.1 Although there is no uniform Indigenous experience, some factors that consistently predispose AI/AN people to health inequity include living in food deserts, crowded, multi-generational households, unemployment, lack of access to technology, distance from health centres and distrust of health-care systems and governments due to historical traumas.3 While data describing COVID-19 in paediatric patients in AI/AN communities are currently limited, extrapolation of the literature allows us to conclude that the burden of the disease may also disproportionately affect the AI/AN children relative to their non-Hispanic white counterparts. Stanley et al. contend that permitting First Nations communities' decision-making power to improve outcomes and effectively partner with governmental and other resources was critical in the success of the Aboriginal and TSI response to COVID-19.1 Based on the achievements of Australian First Nations communities, one may reasonably infer that granting similar trust and control of resources and leadership to AI/AN populations may serve to improve health outcomes in the context of COVID-19 and beyond. All providers who serve AI/AN children must also seek opportunities to partner with local tribes to better understand the priorities, needs and historical experiences of the population, ensure adequate training in culturally sensitive care, thoroughly evaluate the social determinants of health, and leverage cultural assets and resilience to preserve Indigenous identity while improving overall health.4 The Aboriginal and TSI response to COVID-19 offers a stellar example of the strength and success of permitting decision-making by First Nations communities which may provide a means to improve the health of Indigenous populations across the globe.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".