Cross-jurisdictional pandemic management: providers speaking on the experience of Nunavut Inuit accessing services in Manitoba during the COVID-19 pandemic
Bibliographic record
Abstract
Across Canada, the COVID-19 pandemic placed considerable stress on territorial and provincial healthcare systems. For Nunavut, the need to continue to provide access to critical care to its citizens meant that medical travel to provincial points of care (Edmonton, Winnipeg and Ottawa) had to continue through the pandemic. This complexity created challenges related to the need to keep Nunavut residents safe while accessing care, and to manage the risk of outbreaks in Nunavut resultant from patients returning home. A number of strategies were adopted to mitigate risk, including the expansion of virtual care, self-isolation requirements before returning from Winnipeg, and a level of cross-jurisdictional coordination previously unprecedented. Structural limitations in Nunavut however limited opportunities to expand virtual care, and to allow providers from Manitoba to access the Nunavut's electronic medical records of patients requiring follow up. Thus, known and long-standing issues exacerbated vulnerabilities within the Nunavut healthcare system. We conclude that addressing cross-jurisdictional issues would be well served by the development of a more formal Nunavut-Manitoba agreement (with similar agreements with Ontario and Alberta), outlining mutual obligations and accountabilities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.039 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".