Winter Storms and Fall-Related Injuries: Is It Safer to Walk than to Drive?
Bibliographic record
Abstract
Abstract Emergency department visitation data were analyzed using a matched-pair, retrospective cohort method to estimate the effects of winter storms on fall-related injury risks for a midsized urban community in Ontario, Canada. Using a unique definition and classification of winter storm events and dry-weather control periods, relative risks of injury were estimated for total falls and two subcategories (same-level falls involving ice and snow; all other falls) across two storm event types (snowfall only; mixed precipitation). Winter storms were associated with 38% and 102% increases in the mean incidence of same-level falls involving ice and snow during snow events and freezing-rain events, respectively. The incidence of other types of falls was slightly but significantly less during snow events relative to dry-weather control periods. Findings suggest that walking is not safer than driving during winter storms, as same-level falls involving ice and snow accounted for 64% more of the injury burden than motor vehicle collisions. Significant reductions in mean relative risk estimates for fall-related injuries were apparent over the 2009–17 study period indicating possible long-term shifts in exposure, sensitivity, and/or risk-mitigating decisions, actions, and behavior. Consistent and significant effects of government-issued weather warning communications on risk outcomes were not found. Practitioners engaged in developing injury prevention strategies and related public risk messaging, in particular winter weather warnings and advisories, should place additional emphasis on falls and multimodal injury risks in communications related to winter storm 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".