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 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 | 0.002 | 0.013 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".