Sámi community perspectives on the COVID-19 pandemic: a mixed methods case study in Arctic Sweden
Bibliographic record
Abstract
The COVID-19 pandemic posed a grave threat not only to Indigenous people's health and well-being, but also to Indigenous communities and societies. This applies also to the Indigenous peoples of the Arctic, where unintentional effects of public health actions to mitigate the spread of virus may have long-lasting effects on vulnerable communities. This study aim was to identify and describe Sámi perspectives on how the Sámi society in Sweden was specifically affected by the pandemic and associated public health actions during 2020-2021. A mixed-method qualitative case study approach was employed, including a media scoping review and stakeholder interviews. The media scoping review included 93 articles, published online or in print, from January 2020 to 1 September 2021, in Swedish or Norwegian, regarding the pandemic-related impacts on Sámi society in Sweden. The review informed a purposeful selection of 15 stakeholder qualitative interviews. Thematic analysis of the articles and interview transcripts generated five subthemes and two main themes: "weathering the storm" and "stressing Sámi culture and society". These reflect social dynamics which highlight stressors towards, and resilience within, the Sámi society during the pandemic. The results may be useful when evaluating and developing public health crisis response plans concerning or affecting the Sámi society in Sweden.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 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".