Self-Reported Health Impacts and Coping Strategies to Drought in Saskatchewan First Nations Communities
Bibliographic record
Abstract
As the planet continues to warm, precipitation amounts and timing are changing. Predicted increases in frequency and severity of drought in some regions will increase the frequency of dust storms and wildfires, contributing to increased exposure to poor air quality. Poor air quality causes health impacts including respiratory diseases, cardiovascular diseases, stress and mental health outcomes, worsening of pre-existing health conditions, and death. However, relatively few studies exploring future drought-related impacts on health have been conducted in the Canadian context. This study examined self-reported health impacts and coping strategies of members in two First Nation communities in Saskatchewan during poor air quality events. Studying marginalized communities, such as First Nation communities, is essential because these communities face specific exposures due to their ties to the land and are likely to experience significant structural barriers and limits to their adaptation given the impacts of colonization. It is important to work with First Nations communities to understand place-based impacts and culturally appropriate adaptation strategies to effectively inform policy and practice. Elders and Knowledge Holders, Key Informants, and general community members were interviewed to better understand how individuals in First Nation communities in Saskatchewan have experienced and been impacted by drought-induced poor air quality. Common themes emerging include concern for health and wellbeing of youth and Elders in the community, concerns around community preparedness during adverse air quality events, and feelings of community cohesion amongst members for support during these events. Ultimately, the intent of this project is to prioritize Indigenous-specific impacts and actions during poor air quality events in order to enhance evidence-informed policies, education, and awareness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".