Medics, Mercenaries and Miscreants — A review of Canadian Medical Assistance Teams' EMT Type 1 response to the conflict in Ukraine
Bibliographic record
Abstract
Introduction: On February 24, 2022, Russia invaded Ukraine, resulting in Europe’s largest refugee crisis since World War II. More than six million Ukrainians fled the country—half of these to Poland—and one-third of the population was internally displaced. Border points became bottlenecks where fatalities were reported—people risked their lives in long queues and subzero temperatures. Method: This presentation focuses on experiential information obtained during a 17-week deployment of EMT Type 1 both at border points (fixed) and in northwestern Ukraine (mobile). Quantitative and qualitative data were obtained after deployment by online survey with 75 medical, logistical and interpreter volunteers. Results: Initial teams experienced extremely fluid demands and numerous challenges with security, team adherence to COVID-19 protocols, behavioral issues with less experienced volunteers, and collaboration with novel governmental and non-governmental partners to achieve objectives. Conclusion: 1. Deployment to a conflict setting requires adherence to the Incident Command System, with daily security briefings and structured handover between teams at the beginning of each deployment. 2. Strict adherence to well-defined protocols for the prevention and management of emerging infectious risks such as COVID-19 is necessary, along with contingency plans to isolate infected team members. 3. There is a need for standardized pre-deployment vetting, training and orientation of all volunteers—particularly team leaders. 4. Identification of international partners should start pre-deployment and remain a continuous process during deployment.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".