The Impact of Exposure to Previous Disasters on Hospital Disaster Surge Capacity Preparedness in Finland: Hospital disaster surge capacity preparedness
Bibliographic record
Abstract
Abstract Objective: As disasters are rare and high-impact events, it is important that the learnings from disasters are maximized. The aim of this study was to explore the effect of exposure to a past disaster or mass casualty incident (MCI) on local hospital surge capacity planning. Methods: The current hospital preparedness plans of hospitals receiving surgical emergency patients in Finland were collected (n = 28) and analyzed using the World Health Organization (WHO) hospital emergency checklist tool. The surge capacity score was compared between the hospitals that had been exposed to a disaster or MCI with those who had not. Results: The overall median score of all key components on the WHO checklist was 76% (range 24%). The median surge capacity score was 65% (range 39%). There was no statistical difference between the surge capacity score of the hospitals with history of a disaster or MCI compared to those without (65% for both, P = 0.735). Conclusion: Exposure to a past disaster or MCI did not appear to be associated with an increased local hospital disaster surge capacity score. The study suggests that disaster planning should include structured post-action processes for enabling meaningful improvement after an experienced disaster or MCI.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".