The Code Silver Exercise: a low-cost simulation alternative to prepare hospitals for an active shooter event
Bibliographic record
Abstract
Abstract Mass-shooting incidents have been increasing in recent years and Code Silver—the hospital response to a person with a weapon such as an active shooter in many Provinces or States in North America—is quickly shifting from a theoretical safety measure to a realistic scenario for which hospitals must prepare their staff. A Code Silver Exercise (CSE) involving an independent mental practice exercise with written responses to scenarios and questions, followed by a facilitated debrief with all participants, was conceptualized and trialled for feasibility and efficacy. The CSE was piloted as a quality improvement and emergency preparedness initiative in three different settings including in situ within a hospital Emergency Department or Intensive Care Unit, offsite in a large conference room workshop, and online via virtual platform. These sessions took place in 4 different cities in Canada and included 3 academic teaching hospitals. Participants of the in situ and virtual CSE completed pre- and post-simulation surveys which showed improved understanding of Code Silver protocols following participation. The CSE is a reproducible simulation alternative, designed to operationalize a Code Silver policy at a large healthcare institution in a sustainable way. This training model can be administered in multiple settings in-person (in situ or offsite), and virtually, making it versatile and easily accessible for participants. This exercise enables participants to mentally rehearse practical responses to an active shooter in their unique work environments and to discuss ethical and medical-legal implications of their responses during a facilitated debrief with fellow healthcare providers. Implementation of a CSE for training in hospitals may help staff to create a mental schema prior to an active shooter event, and thus indirectly improve the chances of survivability in the event of a real active shooter situation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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 teacher head, 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".