The Impact of Previous Disasters on Hospital Disaster Surge Capacity Preparedness in Finland
Bibliographic record
Abstract
Introduction: In a disaster, the number of victims and severity of injuries may overwhelm the treatment capacity of the local hospital. Surge capacity is the hospital’s ability to receive and treat an increased number of patients. This study aimed to explore if a past disaster or mass casualty incident (MCI) affects local hospital surge capacity preparedness. Method: The current hospital preparedness plans (HPPs) of University and central hospitals receiving surgical emergency patients in Finland were collected (n=28). The HPPs were read and analyzed using the World Health Organization (WHO) hospital emergency checklist tool with eight key components and 67 action items. The scores of key components were compared by percentage of the maximum score. The surge capacity score was compared between the hospitals that had been exposed to a disaster or MCI with those who had not. The effective level was considered as 70% of total points. Results: The overall median score of all key components was 76% (range 24%). The highest score was in command and control (median 93%, range 29%) and the lowest in post-disaster preparedness (median 50%, range 90%). The median surge capacity score was 65% (range 39%). There has been 12 disasters or MCIs during the past 25 years in Finland, all anthropogenic. There was no statistical difference between the surge capacity score of the hospitals with a history of a disaster or MCI compared to those without (65% for both, p=0.735). Conclusion: In Finland, the overall hospital preparedness level is effective with command and control being the best covered area. Surge capacity preparedness was below the effective level and it was not affected by a past disaster or MCI. Present-day challenges with the lack of resources in the health care system, more attention should be drawn to the surge capacity aspect in hospital preparedness plans.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".