FIRST NATIONS LED MENTAL HEALTH RECOVERY IN THE FACE OF ENVIRONMENTAL AND FLOODING JEOPARDY
Bibliographic record
Abstract
Understanding the factors influencing mental and social health after extreme weather events or incremental climate change is crucial to addressing these issues on First Nation reserves in the Canadian prairies. Previous research on an international level has linked climate change to effects on mental health for general populations but, within a First Nations context, the literature base is severely lacking. What little the literature does indicate, however, is that policy in Canada is failing to prevent physical and mental harm to First Nations people from anthropogenically-driven environmental and climate change when compared with general populations. Using interdisciplinary and mixed methodologies, this thesis explores the academic literature linking climate change, disasters, and weather events, and mental health effects, defines and explores environmental mismanagement affecting reserve land, and critically assesses the colonial policies and circumstances that affect First Nations mental health outcomes. The objectives of the present research are executed through systematic review, and qualitative analysis of first-hand experience with flood recovery. The direction of this research is informed by partnerships with Yellow Quill First Nation and James Smith Cree Nation in Saskatchewan. This thesis forms a better understanding of the circumstances of mental health issues in an environmental context and ultimately places itself to inform policy that can reduce environment-related mental health issues in First Nations reserve communities based on an interdisciplinary and community-driven exploration of First Nations led disaster planning, mental health recovery, and environmental management.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".