Community collaboration in the face of the COVID-19 Pandemic: Examples of How Remote First Nations in Northern Ontario Managed the Pandemic
Bibliographic record
Abstract
At the outset of the COVID-19 pandemic, it was believed that Indigenous peoples in Canada would be disproportionately affected due to inequities across social determinants of health as a result of the ongoing processes of colonization. High levels of overcrowding, higher burden of chronic disease, reduced access to clean drinking water, healthcare, and food security in many rural and remote First Nations across northern Canada increased vulnerability to COVID-19. In the Nishnawbe Aski Region of northern Ontario, data from the Sioux Lookout First Nations Health Authority indicates that First Nations communities were able to limit COVID -19 infection and had an overall fatality rate that was lower than the general Canadian population. The focus of this research was to analyze public health data, media reports, and research to determine how the pandemic impacted First Nations throughout northern Ontario. The research highlights that as a direct result of rapid and strength-based responses, First Nations in Northern Ontario have managed the pandemic with limited serious illness, hospitalizations, and fatalities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".