THE WAYS OF COOPERATION BETWEEN DOCTORS OF THE UKRAINIAN DIASPORA IN CANADA AND DOCTORS IN UKRAINE IN THE CONDITIONS OF MODERN CHALLENGES
Bibliographic record
Abstract
In the article, the current state of cooperation between Canadian Ukrainians and Ukrainian doctors analyzes. The urgency of modern challenges for cooperation is determined, in particular: pandemic restrictions due to the rapid spread of Covid-19 virus and a sharp restriction of direct professional contacts; the politicization of professional international cooperation in the conditions of Russian aggression; bureaucracy on the part of the management of medical institutions of Ukraine, unfinished state regulation of charitable organizations and international cooperation; insufficient development of charity in Ukraine. The activity of the main Cooperation programs between doctors Canadian Ukrainians and Ukrainian doctors is analyzed. The main focus of the article is on the analysis of the effectiveness of the Canadian-Ukrainian Pediatric Fellowship Program. The Medical Director of the Program is Professor James Rutka, and the Program Coordinator is Professor Myroslava Romach. The stages of organizing training meetings within the Program are indicated: planning and preparation for the meeting (goal setting and detailed planning); choice of locations, according to pre-thought-out criteria; the meeting itself (lectures and practical training, joint operations in 7 cities of Ukraine); further observation, assessment of the impact and results of the meeting, as well as planning of long-term cooperation. Analysis of the concrete results of cooperation is one of the most important stages of international cooperation, as it not only increases its efficiency but also ensures the targeted use of funds. It is determined that important factors that increase the effectiveness of interstate programs in the field of medicine are awareness of cooperation at the diplomatic level, coordinated cooperation at the local level, selection of reliable partners for cooperation, systematic contacts, activities, targeted, premeditated assistance, careful selection of applicants, changes in the means of communication in today's challenges. The work of the above-mentioned medical Ukraine Paediatric Fellowship Program can serve as an example of the organization of international cooperation in various directions of social and political life of modern Ukraine, and a guarantee of its high efficiency in clear strategic planning.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".