Mitigating the impact of the COVID-19 pandemic on Inuit living in Manitoba: community responses
Bibliographic record
Abstract
We document community responses to the COVID-19 pandemic among Inuit living in the province of Manitoba, Canada. This study was conducted by the Manitoba Inuit Association and a Council of Inuit Elders, in partnership with researchers from the University of Manitoba. We present findings from 12 health services providers and decision-makers, collected in 2021.Although Public Health orders led to the closure of the Manitoba Inuit Association's doors to community events and drop-in activities, it also created opportunities for the creation of programming and events delivered virtually and through outreach. The pandemic exacerbated pre-existing health and social system's shortcomings (limited access to safe housing, food insecurity) and trauma-related tensions within the community. The Manitoba Inuit Association achieved unprecedented visibility with the provincial government, receiving bi-weekly reports of COVID-19 testing, results and vaccination rates for Inuit. We conclude that after over a decade of advocacy received with at best tepid enthusiasm by federal and provincial governments, the Manitoba Inuit Association was able effectively advocate for Inuit-centric programming, and respond to Inuit community's needs, bringing visibility to a community that had until then been largely invisible. Still, many programs have been fueled with COVID-19 funding, raising the issue of sustainability.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".