Assessment of Hospital Disaster Readiness: A Tertiary Care Teaching Hospital Experience
Bibliographic record
Abstract
preparedness consisting of tabletop drills and disaster simulation.Based on the Incident Command System (ICS) framework, our system prepares medical providers to respond independently to country level disasters.Background: Disaster response remains an important component of emergency preparedness internationally.To this end, the Incident Command System (ICS) provides a standardized approach to the command, control and coordination of emergency response.Methods: A two-day workshop was conducted with medical providers in Bangalore, India that used serial disaster simulations to improve disaster response using the Incident Command System (ICS).Through increasing responsibility and selfdirected tabletops, the participants (doctors, medical students, nurses and police) gained the skills to respond independently to a simulated countrywide disaster.After the exercise, they were asked to grade the usefulness of simulation and lectures.Results: Forty-four providers responded to the questionnaire, all of which (n = 44, 100%) recommended the course.They graded the final disaster drill as most useful (n = 36, 82%) and also graded lectures from topic experts as useful (n = 36, 83%).Based on qualitative written feedback, participants felt drills helped them in communication and leadership.Conclusion: This novel teaching modality, using simulation and tabletop drills is an effective tool to teach the Incident Command System (ICS) to medical providers.Participants felt they benefitted from training and would respond better to future disasters.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".