Impacts of COVID-19 on Indigenous Communities in Canada.
Bibliographic record
Abstract
Background: The coronavirus disease (COVID-19) is an infectious and potentially deadly virus with growing research examining the mechanism of outbreak and community spread. It is commonly spread through respiratory droplets. This virus has caused a global pandemic declared on March 11, 2020, which has initiated and continues to cause negative health and wellness effects worldwide. This pandemic had led to a closure of social infrastructures, local businesses, and financial instability. Purpose: The purpose of this literature review was to explore the experiences and responses to COVID-19 for Indigenous communities in Canada, specifically looking at the physical health and wellness in these communities. Methods: A literature review was conducted in June 2021. An infographic was created after a review of the literature. Results: Many Indigenous communities in Canada experience the disproportional effects from historical and ongoing health pandemics due to factors such as social determinants of health, the effects of intergenerational trauma, and systemic racism. Indigenous communities have found strengths in community-led initiatives that focus on promoting overall spiritual, mental, physical, and emotional wellbeing. Conclusion: Indigenous communities in Canada are successfully persevering through the COVID-19 pandemic by maintaining culturally relevant connections with family-centered and land-based approaches to physical health and wellness that have been practiced for generations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".