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Record W2682882715 · doi:10.12927/hcpol.2017.25100

Retention Patterns of Canadians Who Studied Medicine Abroad and Other International Medical Graduates

2017· article· en· W2682882715 on OpenAlexaffvenueabout
Maria Mathews, Rima Kandar, Steve Slade, Yanqing Yi, Ivy Lynn Bourgeault

Bibliographic record

VenueHealthcare policy · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsInstitute of Gender and HealthCanadian Medical AssociationGovernment of CanadaRoyal College of Physicians and Surgeons of CanadaMemorial University of Newfoundland
Fundersnot available
KeywordsImmigrationMedical educationDemographic economicsMedicinePolitical sciencePsychologyEconomics

Abstract

fetched live from OpenAlex

OBJECTIVES: Are Canadians who study abroad (CSAs) more likely to stay in Canada than other international medical graduates (IMGs)? We looked at retention patterns of CSAs and immigrant IMGs who completed post-graduate medical education (PGME) training in Canada to describe the proportion and predictors of those working in Canada and in rural communities in Canada in 2015. METHODS: We linked the National IMG Database to Scott's Medical Database to track the work locations of CSAs and immigrant IMGs in 2015. RESULTS: Of the 1,214 IMGs who entered PGME training in Canada between 2005 and 2011, most were working in Canada in 2015 (88.0%). Relatively few IMGs worked in rural communities (9.1%). There were no differences in work location patterns of CSAs and immigrant IMGs. CONCLUSION: Contrary to what CSA advocates suggest, CSAs have the same retention patterns as immigrant IMGs. PGME admission policies should treat all IMGs in the same manner, regardless of their citizenship or residency before medical school.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.103
GPT teacher head0.522
Teacher spread0.420 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations11
Published2017
Admission routes3
Has abstractyes

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