Prehospital Whole Blood Transfusion Training in Ukraine: A Case Study Highlighting the Efficacy of Collaboration and Advocacy
Bibliographic record
Abstract
Introduction: Early in the Russian-Ukrainian conflict, the Ukrainian Ministry of Health (MoH) implemented policy reform to allow for pre-hospital whole blood transfusion (pWBT). Team Rubicon (TR) worked with a multinational group of experts to disseminate training that accelerated the implementation of pWBT across the country. Method: TR utilized an assess, align, and act (A3) approach to drive the pWBT implementation. TR established relationships with Ukrainian providers to understand current needs, restrictions, and protocols for pWBT. TR aligned pWBT advocacy efforts, working with the disaster medicine program at Ivano-Frankivsk Medical National University to create a local lead advocate. Existing and novel coordination mechanisms were used to unite and inform MoH, World Health Organization, Non-Governmental Organizations, and local health systems. Finally, TR coordinated a multispecialty, multi-national team of healthcare providers who developed and delivered a training package in alignment with national guidelines utilizing a combination of didactics, videos, and demonstrations. From August to October of 2022, TR conducted pWBT trainings across Ukraine. Pre- and post-surveys were utilized to determine comfort with pWBT and usefulness of the training. Results: TR emerged as the point of reference for pWBT in Ukraine. 109 individuals from over 14 organizations were trained. Participants included 69 physicians, 23 paramedics, 7 nurses, and 10 other professionals. 95% of those surveyed had not received prior pWBT training. Participants reported increased comfort levels, with average pre- and post-course comfort scores of 1.7 and 3.2 (4=very comfortable), respectively. The majority of participants found the training useful (average score of 3.8, 4=very useful). Feedback demonstrated high satisfaction ratings and an increased awareness of the regulatory changes. Conclusion: TR utilized the A3 model to drive a coalition that supported policy reform and trauma system improvements in Ukraine. TR’s ability to leverage international medical expertise, work collaboratively with MoH, and provide material resources supported local implementation of pWBT.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".