Global distribution and career outcomes of international fellows trained in Canadian gynecologic oncology programs
Bibliographic record
Abstract
• Canada has played a key role in the training of international graduates in gynecologic oncology. • The global education goal was achieved, as 85% of trainees returned to their home country. • International fellows reached important landmarks in terms of academic, clinical and research accomplishments. • Most international fellows reported a high rate of satisfaction with their training. • Since none of the trainees were from low-income countries, different training models might be more appropriate for those settings. We assessed the global distribution and academic, administrative and research outcomes of international fellows (IFs) trained in Canadian gynecologic oncology (GO) programs. A web-based survey was sent to IFs who completed GO training in Canada. Using the Web of science database, we identified the publication list, citation record and H-index of IFs and classified them according to their region of practice: high-income countries (HIC), middle income countries (MIC), and low-income countries (LIC). From 1996 to 2020, 81 IFs from 23 countries were trained in English-speaking (62,9%) and French-speaking Canadian universities (37,1%). Most IFs came from HIC (87,6%) and none from LIC. Only 12 IFs (14,8%) are now practicing in Canada. Of the 55 IFs who completed the survey (response rate: 67,9%), the majority (58,2%) reported working in an academic hospital and 29,1% were holding an executive position in a national scholar organization. IFs participated in mentoring residents (96.4 %) and medical students (83,6%) and 36,3% initiated a GO fellowship program in their home country. 67,3% of IFs were involved in international research collaboration and 52,7% participated in international clinical trials. The mean number of publications (22,36 vs 7,75, p = 0.007), citations (369,15 vs 45,12 p = 0.0006) and H-Index (6,88 vs 2,37 p = 0.0001) were significantly higher among IFs working in HIC compared to those in MIC. Most IFs (98,2%) recommended their Canadian GO fellowship program to a colleague from their home country. Most IFs trained in Canadian GO fellowship programs returned to their home countries and achieved important milestones in terms of academic, clinical and research accomplishments.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".