Preserving rural school health during the COVID-19 pandemic: Indigenous citizen scientist perspectives from a qualitative study
Bibliographic record
Abstract
This qualitative study is part of Smart Indigenous Youth, a digital health community trial involving rural schools in Saskatchewan, Canada. Secondary school administrators and educators were engaged as citizen scientists in rural Indigenous communities to understand rapid decision-making processes for preserving school health during the COVID-19 pandemic, and to inform evidence-based safe school policies and practices. After COVID-19 restrictions were implemented, key informant interviews and focus groups were conducted with school administrators and educators, respectively, to understand the impact of school responses and decision-making processes. Two independent reviewers conducted thematic analyses and compared themes to reach consensus on a final shortlist. Four main themes emerged from the administrator interviews, and six main themes were identified from the educator focus group discussions which revealed a pressing need for mental health supports for students and educators. The study findings highlight the challenges faced by schools in rural and remote areas during the COVID-19 pandemic, including school closures, students' reactions to closures, measures taken by schools to preserve health during the pandemic, and different approaches to implement for future closures. Citizen scientists developed a set of recommendations, including the need for structured communication, reflection meetings, adequate funding, and external monitoring and evaluation to guide evidence-based safe school policies and practices during the pandemic.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".