Temperature and place associations with Inuit mental health in the context of climate change
Bibliographic record
Abstract
BACKGROUND: Climate change has important implications for mental health globally. Yet, few studies have quantified the magnitude and direction of associations between weather and mental health-related factors, or assessed the geographical distribution of associations, particularly in areas experiencing rapid climatic change. This study examined the associations between air temperature variables and mental health-related community clinic visits across Nunatsiavut, Labrador, Canada, and the place-specific attributes of these associations. METHODS: Daily de-identified community clinic visit data were collected from the provincial electronic health recording system and linked to historical weather data (2012-2018). A multilevel, multivariable negative binomial regression model was fit to investigate associations between temperature variables and mental health-related community clinic visits across the region, adjusting for seasonality as a fixed effect and community as a random effect. A multivariable negative binomial model was then fit for each Nunatsiavut community, adjusting for seasonality. RESULTS: = 1.73, 95% CI = 1.02-2.94), and the incidence rate of mental health-related clinic visits was lower when the number of consecutive days within -5 to 5ᵒC ranges (i.e. temperatures considered to be critical to land use) increased (IRR = 0.96; 95% CI = 0.94-0.99), adjusting for seasonal and community effects. Community-specific models, however, revealed that no two communities had the same association between meteorological conditions and the incidence rate of daily mental health-related visits. DISCUSSION: Regionally, longer periods of warm temperatures may burden existing healthcare resources and shorter periods of temperatures critical to land use (i.e. -5 to 5ᵒC) may present enjoyable or opportunistic conditions to access community and land-based resources. The heterogeneity found in temperature and mental health-related clinic visits associations across Nunatsiavut communities demonstrates that place quantitatively matters in the context of Inuit mental health and climate change. This evidence underscores the importance of place-based approaches to health policy, planning, adaptation, and research related to climate change, particularly in circumpolar regions such as Nunatsiavut where the rate of warming is one of the fastest on the planet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".