Public health restrictions, directives, and measures in Arctic countries in the first year of the COVID-19 pandemic
Bibliographic record
Abstract
Beginning January of 2020, COVID-19 cases detected in Arctic countries triggered government policy responses to stop transmission and limit caseloads beneath levels that would overwhelm existing healthcare systems. This review details the various restrictions, health mandates, and transmission mitigation strategies imposed by governments in eight Arctic countries (the United States, Canada, Greenland, Norway, Finland, Sweden, Iceland, and Russia) during the first year of the COVID-19 pandemic, through 31 January 2021s31 January 2021. We highlight formal protocols and informal initiatives adopted by local communities in each country, beyond what was mandated by regional or national governments. This review documents travel restrictions, communications, testing strategies, and use of health technology to track and monitor COVID-19 cases. We provide geographical and sociocultural background and draw on local media and communications to contextualise the impact of COVID-19 emergence and prevention measures in Indigenous communities in the Arctic. Countries saw varied case rates associated with local protocols, governance, and population. Still, almost all regions maintained low COVID-19 case rates until November of 2020. This review was produced as part of an international collaboration to identify community-driven, evidence-based promising practices and recommendations to inform pan-Arctic collaboration and decision making in public health during global emergencies.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".