Mass-Casualty Events at Schools: A National Preparedness Survey
Bibliographic record
Abstract
OBJECTIVE: Recent school shootings and terrorist events have demonstrated the need for well-coordinated planning for school-based mass-casualty events. The objective of this study was to document the preparedness of public schools in the United States for the prevention of and the response to a mass-casualty event. METHODS: A survey was mailed to 3670 school superintendents of public school districts that were chosen at random from a list of school districts from the National Center for Education Statistics of the US Department of Education in January 2004. A second mailing was sent to nonresponders in May 2004. Descriptive statistics were used for survey variables, and the chi2 test was used to compare urban versus rural preparedness. RESULTS: The response rate was 58.2% (2137 usable surveys returned). Most (86.3%) school superintendents reported having a response plan, but fewer (57.2%) have a plan for prevention. Most (95.6%) have an evacuation plan, but almost one third (30%) had never conducted a drill. Almost one quarter (22.1%) have no disaster plan provisions for children with special health care needs, and one quarter reported having no plans for postdisaster counseling. Almost half (42.8%) had never met with local ambulance officials to discuss emergency planning. Urban school districts were better prepared than rural districts on almost all measures in the survey. CONCLUSIONS: There are important deficiencies in school emergency/disaster planning. Rural districts are less well prepared than urban districts. Disaster/mass-casualty preparedness of schools should be improved through coordination of school officials and local medical and emergency officials.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".