Lament for the Land: On the Impacts of Climate Change on Mental and Emotional Health and Well-Being in Rigolet, Nunatsiavut, Canada
Bibliographic record
Abstract
As the impacts from anthropogenic climate change are felt around the globe, people are increasingly exposed to changes in weather, temperature, wildlife and vegetation patterns, and water and food quality and availability. These changes impact human health and well-being, and resultantly, climate change has been identified as the biggest global health threat of the 21st Century. Recently, the mental health impacts emerging from these changes are gaining increasing attention globally. Research indicates that changes in climate and environment, and the subsequent disruption to the social, economic, and environmental determinants of mental health, are causing increased incidences of mental health issues, emotional responses, and large-scale socio-psychological changes. Inuit in Northern Canada have been experiencing the most rapid climatic and environmental changes on the planet: increased seasonal temperatures; decreased snow and ice quality, stability, and extent; melting permafrost; decreased water levels in ponds and brooks; increased frequency and intensity of storms; later ice formation and earlier ice break-up; and alterations to wildlife and vegetation. These changes are decreasing the ability of Inuit to hunt, trap, fish, forage, and travel on the land, which directly disrupts their health, and is negatively impacting mental and emotional health and well-being. Through a multi-year, exploratory, qualitative case study conducted in Nunatsiavut, Labrador, Canada representing the first research to examine the mental and emotional health impacts of climate change within a Canadian Inuit context, Inuit indicated that climate change was impacting mental health through seven interrelated pathways: strong emotional responses; increased reports of family stress; increased reports of drug and alcohol usage; increased reports of suicide ideation and attempts; the amplification of previous traumas and mental health stressors; decreased place-based mental solace; and land-based mourning due to a changing environment. Data for this research was drawn from 85 in-depth interviews and 112 questionnaires conducted between October 2009 and October 2010. These findings indicate the urgent need for more research on climate-change-related mental health impacts and emotio-mental adaptive processes, for more mental health support to enhance resilience to and assist with the mental health impacts of climate change, and for more mitigation and adaptation policies to be implemented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".