Increasing Trend of Studying Abroad for Residency Training Among Medical Students
Bibliographic record
Abstract
Physician emigration from developing and underdeveloped countries to developed countries is a growing problem that is taking place in current times and is an obstacle in achieving global health. Social circumstances, economic demands, as well as modern medical technologies and desire to pursue better career opportunities, are all driving factors for medical graduates’ emigration. This study investigates Turkish medical students’ intentions of pursuing residency training abroad and explores associated factors that play a significant role in instilling the tendency of moving overseas. A cross-sectional study was conducted at Ege University, Faculty of Medicine, İzmir, Turkey. Students’ future specialty intentions, preferred country for residency training, and the contributing factors in their decisions were questioned via an online survey questionnaire. Out of 617 students included in the study analysis, 183 (29.7%) expressed their desire for going abroad for residency training and Germany, United States of America, United Kingdom, and Canada were the top preferred destinations where 40 (25.3%) reported planning to stay permanently. Mother’s level of education and student’s training/ understanding of advanced levels of any foreign language were found significantly associated with the developed intention of going abroad for residency training. Qualitative analysis revealed better “living standards/conditions” was the most frequently uttered reason for studying abroad. It’s vital to know reasons out why medical school graduates want to go to another country. A better understanding of this issue will aid in developing actions to reduce the proclivity of medical graduates to relocate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".