Canadian Northern and Indigenous health policy responses to the first wave of COVID-19
Bibliographic record
Abstract
Aims: This study aimed to compare COVID-19 health policy and programme responses in 16 Northern and Indigenous regions in Canada. The goal was to summarise strategies used to mitigate the initial spread of the pandemic while highlighting aspects that reflect Indigenous values. Methods: A scoping review of grey literature was completed, focusing on territorial, regional health authority, and community level websites. Further media analysis was conducted to reach saturation regarding policy changes and programmes implemented to prevent transmission, improve health communication, access testing, provide health services effectively, secure borders, and provide financial assistance. Common responses were mapped on the Women’s College Hospital’s Wholistic Framework for Safe Wellness to identify aspects that reflected Indigenous values. This framework utilises the medicine wheel to discuss physical health (body), ceremony (spirit), community health (heart), and assessment (mind). Results: The Women’s College Hospital’s Wholistic Framework for Safe Wellness quadrants of the body, spirit and heart were covered by most regions via health communication efforts, adaptations to traditional practices, and continuation of care during the pandemic, respectively. It was found that 13 regions had pandemic responses adapted for Indigenous populations. Conclusions: The responses in each Northern region show that protecting each community was a priority; however, policies and programmes were developed as a kaleidoscope of what can be done quickly and evaluated later. Assessment, risk, and prevention, covered by the mind quadrant of the Women’s College Hospital’s Wholistic Framework for Safe Wellness, were missing in initial emergency responses. Increasing capacity for emergency management in Northern and Indigenous regions will require contingency planning that acknowledges and builds off traditional knowledge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".