Recommendations for Improving Civilian-Military Disaster Coordination: A Systematic Review of an International Bio-Disaster Response Utilizing Interoperability Assessment Models
Bibliographic record
Abstract
Background/Introduction: Disasters strain coordination efforts between groups. Interoperability is best assessed while in process, but retrospective analysis can also illuminate problems and identify solutions. COVID-19 created an international public health crisis that required civilian-military response in many locations, creating an opportunity to evaluate interoperability of multiple international systems at a single moment in time confronting a single crisis. Objectives: This project uses three published interoperability models to identify interoperability activities during the COVID-19 pandemic. That data was then utilized to assess the interoperability effectiveness. The data was also utilized to develop a framework for assessing a group’s current interoperability and assist with improvement goals. Method/Description: Papers on civilian-military interoperability during COVID-19 were identified utilizing a search of medical literature. They were then assessed using three interoperability models: Joint Emergency Services Interoperability Program (JESIP), Organizational Interoperability Maturity Model (OIMM), and the Homeland Security Interoperability Continuum (HSIC). Results/Outcomes: Of the 48 articles discussing interoperability criteria, the most common coordination criteria were shared situational awareness, joint understanding of risk, and standard operating procedures. The least likely interoperability criteria seen during international civilian-military COVID-19 disaster responses were co-location, preparedness, shared technology, prior training exercises, and previous experience. Utilizing this data, a combined interoperability assessment model was created for organizations to utilize to evaluate and improve their current level of interoperability. Conclusion: Disaster focused organizations with different cultures yet potential future interactions should perform an initial interoperability self-assessment to determine their current level of coordination. They should then follow the next steps for improving interoperability before the next disaster strikes.
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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 | 0.063 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.041 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".