Breaking the Ice: Exploring the Link Between Glaciers and Mental Well-being
Bibliographic record
Abstract
Background Glaciers are integral in maintaining hydrological cycles, moderating oceanic levels, and preserving valuable ecosystems. Cryospheric regions are often overlooked in evaluating the environmental factors affecting mental health. This study investigates the potential influence of glacial presence and melt behaviour on global mental health, particularly among marginalized communities. Methods National suicide rates of general population and specific age categories were gathered from World Health Organization between 2012-19. Glacial data was sourced from the World Glacier Monitoring Service, and Randolph Glacier Inventory. Wilcox testing was conducted to identify mean suicide rates across countries with and without glaciers. Pearson and Spearman correlation testing were employed to identify relationships between melt rate indicators and suicide rates. Results Over the entire eight-year duration, countries with the existence of glaciers revealed a notably higher suicide rate (p-value of 0.0001). Children aged 5-15 years old demonstrated a consistently higher suicide rate amongst countries with glacial bodies (p-value between 0.020-0.037). A positive correlation between regional suicide rates and glacial area was revealed, except in low-latitude countries. Although melt rate variability showed no significant correlation with suicide statistics, Greenland was the only country to demonstrate a negative relation among all populations. Conclusions To address the ongoing impacts of the climate crisis, further research is necessary to develop an inclusive framework that acknowledges the unique challenges faced by communities living in cryospheric regions. This study is the tip of the iceberg, recognizing the importance of inclusivity in addressing the mental health implications of climate change in these environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".