A qualitative exploration of the impacts of COVID-19 in two rural Southwestern Alaska communities
Bibliographic record
Abstract
This manuscript presents a qualitative exploration of the experiences of people in two Southwestern Alaska communities during the emergence of COVID-19 and subsequent pandemic response. The project used principles of community based participatory research and honoured Indigenous ways of knowing throughout the study design, data collection, analysis, and dissemination. Data was collected in 2022 through group and individual conversations with community members, exploring impacts of the COVID-19 pandemic. Participants included Elders, community health workers, Tribal council members, government employees, school personnel, and emergency response personnel. Notes and written responses were coded using thematic qualitative analysis. The most frequently identified themes were 1) feeling disconnected from family, friends, and other relationships, 2) death, 3) the Tribal councils did a good job, and 4) loss of celebrations and ceremonies. While the findings highlighted grief and a loss of social cohesion due to the pandemic, they also included indicators of resilience and thriving, such as appropriate and responsive local governance, revitalisation of traditional medicines, and coming together as a community to survive. This case study was conducted as part of an international collaboration to identify community-driven, evidence-based 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".