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Record W3000257485 · doi:10.36834/cmej.68175

Canadians studying medicine abroad and their journey to secure postgraduate training in Canada or the United States

2020· article· en· W3000257485 on OpenAlexaffvenueabout
Ilona Bartman, John R. Boulet, Sirius Qin, M. Ian Bowmer

Bibliographic record

VenueCanadian Medical Education Journal · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsMedical Council of Canada
Fundersnot available
KeywordsWorkforceLicensurePolitical scienceCommissionFamily medicineMedicineMedical educationLibrary science

Abstract

fetched live from OpenAlex

BACKGROUND: From national and international workforce perspectives, Canadians studying medicine abroad (CSAs) are a growing provider group. Some were born in Canada whereas others immigrated as children. They study medicine in various countries, often attempting both American and Canadian medical licensure pathways. METHODS: Using data from the Educational Commission for Foreign Medical Graduates (ECFMG) and the Medical Council of Canada (MCC), we looked at CSAs who attempted to secure residency positions in both Canada and the United States. We detailed the CSAs' countries of birth and medical education. We tracked these individuals through their postgraduate education programs to enumerate their success rate and categorize the geographic locations of their training. RESULTS: The majority of CSAs study medicine in one of 10 countries. The remainder are disbursed across 88 other countries. Most CSAs were born in Canada (62%). Approximately 1/3 of CSA from the 2004-2016 cohort had no record of entering a residency program in Canada or the United States (U.S.). Recently graduated CSAs were most likely to secure residency training in Ontario and New York. CONCLUSION: Many CSAs attempt to secure residency training in both Canada and the U.S. Quantifying success rates may be helpful for Canadians thinking about studying medicine abroad. Understanding the educational pathways of CSAs will be useful for physician labour workforce planning.

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)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.284
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0100.000

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 head0.087
GPT teacher head0.425
Teacher spread0.338 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations6
Published2020
Admission routes3
Has abstractyes

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