International Student Mobility: New and Emerging Patterns in Medical Education in the Global South
Bibliographic record
Abstract
Abstract This chapter examines the evolving dynamics of international student mobility (ISM) in the context of medical education, with a particular focus on Indian students pursuing undergraduate medical degrees in non-Anglophone countries such as Russia, Ukraine and China. It highlights the shift from traditional South-North migration patterns to a more diversified landscape, where Asian countries are becoming significant destinations for medical education. The chapter explores the factors influencing Indian students’ decision to study abroad, including the affordability of education and the perception of higher quality in foreign institutions. It also addresses the challenges faced by these students, such as the need to pass the foreign medical graduate examination (FMGE) to practice in India and the impact of geopolitical events like the war in Ukraine and the COVID-19 pandemic on their education and career trajectories. The study draws on various data sources, including government records and media narratives, to provide a comprehensive overview of the current trends and implications of ISM in medical education within the Global South, offering insights into the policy ramifications for both sending and receiving countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".