MétaCan
Menu
Back to cohort
Record W4409179504 · doi:10.1007/978-981-97-9715-8_14

International Student Mobility: New and Emerging Patterns in Medical Education in the Global South

2025· book-chapter· en· W4409179504 on OpenAlexaff
Arokkiaraj Heller, S. Irudaya Rajan, Margaret Walton‐Roberts

Bibliographic record

VenueInternational perspectives on migration · 2025
Typebook-chapter
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsBalsillie School of International Affairs
Fundersnot available
KeywordsGlobal SouthGlobal educationMathematics educationGeographyEconomic geographyPsychologyPedagogy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.675
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.456
Teacher spread0.430 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInternational perspectives on migrationSame topicGlobal Health Workforce IssuesFrench-language works237,207