An Active Shooter in Your Hospital: A Novel Method to Develop a Response Policy Using In Situ Simulation and Video Framework Analysis
Bibliographic record
Abstract
Hospital shootings (Code Silver) are events that pose extreme risk to staff, patients, and visitors. Hospitals are faced with unique challenges to train staff and develop protocols to manage these high-risk events. In situ simulation is an innovative technique that can evaluate institutional responses to emergent situations. This study highlights the design of an active shooter in situ simulation conducted at a Canadian level-1 trauma center to test a Code Silver active shooter protocol response. We further apply a modified framework analysis to extract latent safety threats (LSTs) from the simulation using ethnographic observation of the response by law enforcement, hospital security, logistics, and medical personnel.The video-based framework analysis identified 110 LSTs, which were assigned hazard scores, highlighting 3 high-risk LSTs that did not have effective control measures or were not easily discoverable. These included lack of security during patient transport, inadequate situational awareness outside the clinical area, and poor coordination of critical tasks among interprofessional team members. In situ simulation is a novel approach to support the design and implementation of similar events at other institutions. Findings from ethnographic observations and a video-based analysis form a structured framework to address safety, logistical, and medical response considerations.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".