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The value of observerships abroad: Lessons from UA-MED supporting Ukrainian cancer care during the war.

2025· article· en· W4410812837 on OpenAlexaboutno aff
Nataliya Kovalchuk, Andriy Beznosenko, Vitaliy Poylin, Andrey Zinchuk, Alla Vash‐Margita, Arman Kacharian, Rostyslav Semikov, Mark M. Mims, Douglas A. Davis, Nataliya V. Uboha, Natalka Suchowerska, Viktor Iakovenko, Jacqueline S. Hart, Mark C. Poznansky, Serguei Melnitchouk, Nelya Melnitchouk, Olga Maihutiak

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineValue (mathematics)UkrainianCancerFamily medicineOncologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.099
GPT teacher head0.568
Teacher spread0.469 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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