Beyond “Snow Shoveler's Infarction”: Broadening perspectives on winter health risks
Bibliographic record
Abstract
Abstract Winter constitutes a significant threat to humans via damages, injuries, and fatalities in mid and high latitude environments. Much of the research into the health impacts of winter centers on urban areas in the snow climates of North America. When considering how humans are vulnerable to winter hazards, the legend of Snow‐Shoveler's Infarction dominates the public's assessment of winter health risks. This article seeks to broaden the understanding of winter hazards by summarizing the diversity of impacts on human health including frostbite, hypothermia, traffic accidents, slips, and falls, unintentional carbon monoxide poisoning in addition to injuries, and fatalities associated with snow removal. Further, social determinants such as poverty and social isolation are identified as being associated with negative health outcomes. The disproportionate health impacts of winter are also summarized, thus revealing how some groups, particularly the disadvantaged, the elderly and those with preexisting health issues in urbanized snow climates of the United States and Canada, are more vulnerable to winter hazards.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".