Community emergency department utilization following a natural disaster (the Goderich Tornado)
Bibliographic record
Abstract
Introduction: On 21 August 2011 an F3 tornado hit the Canadian town of Goderich, Ontario, leaving 40 people injured and one dead.Specific medium-term changes in utilization of health care following a disaster have not been analyzed in medical literature.Documenting the emergency department utilization through this subacute period would be helpful to enable institutions and healthcare practitioners to be better prepared for future events.Methods: A medical chart review was conducted at the Alexandra Marine and General Hospital in Goderich.All emergency department visits made during the 30 days after the Tornado in 2011 (intervention group), 30 days prior to the tornado in 2011 (primary control group), and during the similar calendar period of 30 days after the tornado in 2010 (seasonal control group) were reviewed.Medical diagnoses of all patients who presented at the emergency department were collected and compared.Results: Fewer people presented to the emergency department following the tornado than during the control periods, and those who did were significantly older than those who presented in the control periods (p<0.001).A significantly greater number of patients presented with undiagnosed medical problems, many came to refill their medications, and significantly fewer people left the emergency department without being seen (p<0.001).Conclusions: This study identifies the medical conditions that are most likely to be seen in an emergency department following a tornado in a rural Ontario community.This information serves to inform the medical community and other hospitals how to increase their level of preparedness should a comparable disaster occur again in the future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".