The value of observerships abroad: Lessons from UA-MED supporting Ukrainian cancer care during the war.
Bibliographic record
Abstract
9036 Background: This study evaluates the influence of international observerships organized by the coalition of healthcare professionals from academic institutions - the Ukrainian Alliance for Medical Exchange and Development (UA-MED) - on the professional development, knowledge transfer, and clinical practice improvement of Ukrainian oncology professionals during the war. Methods: A total of 126 international observerships were facilitated for various Ukrainian medical professionals across 17 participating institutions the US, Canada, Europe, and Australia. A survey was administered to assess the impact of observerships on oncology care in Ukraine, focusing on procedural knowledge gained, lessons learned, and challenges faced when implementing new techniques upon return. Results: Eighty-six respondents participated in the survey. Seventy-three percent of respondents were oncology professionals, including 30.1% radiation oncologists, 31.7% surgeons, 15.9% medical oncologists, and 14% medical physicists. The median duration of the observerships was 4 weeks with 79.7% observers attending a professional conference. The average satisfaction score for the observerships was 9.6 ± 0.7 out of 10. Importantly, 93% of respondents reported a shift in their perception of how to practice medicine, 90% learned new procedures and techniques, and 71.2% implemented these new procedures upon returning to Ukraine. However, despite this progress, significant barriers to implementation were encountered, including lack of material resources (70.7%), human resources (43.1%), and support from department leadership (43.1%) and colleagues (32.8%). Encouraged to disseminate their knowledge, participants provided informal training to colleagues (78.0%), prepared presentations for their institutions (69.5%), national conferences (44.1%), and incorporated learned materials into educational lectures (49.2%). Notably, 83.0% of participants maintained ongoing mentorship contact with their training institutions. Key institutional advancements included transition from Co-60 to linear accelerators at few centers, the launch of an allogeneic bone marrow transplant program, and the development of educational programs across various specialties. Participants emphasized improved confidence in their clinical decision-making and highlighted the value of multidisciplinary team approaches they observed abroad. Conclusions: The international observerships played a crucial role in enhancing the skills and knowledge of Ukrainian cancer care professionals during the war. Despite the ongoing conflict, significant improvements were made in clinical practice, medical education, and the implementation of new procedures. The success of these observerships underscores the potential for similar programs to be replicated in other LMICs/UMICs.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".