Strengthening Active Shooter Response Through Interprofessional Training: The Role of Health Security Teams in Healthcare Systems
Bibliographic record
Abstract
Background: Active shooter incidents in healthcare settings have emerged as a critical health security challenge, disrupting care delivery and threatening staff and patient safety. Hospitals, designed for accessibility and continuous operation, are increasingly recognized as vulnerable “soft targets” for firearm-related violence. Aim: This study aims to examine the operational complexities of active shooter preparedness in healthcare facilities and propose interprofessional strategies to strengthen response and resilience. Methods: A comprehensive review of epidemiologic data, regulatory frameworks, and case analyses was conducted, focusing on U.S. hospital shootings and global hybrid-targeted violence (HTV) events. The study synthesizes evidence from occupational safety guidelines, law enforcement protocols, and healthcare contingency planning literature. Results: Findings reveal that hospital shootings are often targeted, relational, and concentrated in high-risk zones such as emergency departments and outpatient clinics. Five typologies of violence—criminal intent, patient-related, worker-to-worker, domestic spillover, and ideological—shape risk profiles. HTV incidents, involving coordinated multi-weapon tactics, pose additional threats requiring all-hazards preparedness. Effective response hinges on facility-specific contingency plans, rapid communication systems, and simulation-based training. Conclusion: Active shooter preparedness in healthcare demands a multidimensional approach integrating physical security, behavioral threat assessment, and interprofessional collaboration. Continuous evaluation, scenario-based drills, and robust communication protocols are essential to mitigate harm and sustain clinical operations during violent crises.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".