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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. RésuméObjectifs : Les Canadiens qui ont étudié à l'étranger (CEE) sont-ils plus susceptibles de rester au Canada que les autres diplômés internationaux en médecine (DIM)?Nous avons examiné les schémas de rétention du personnel chez les CEE et chez les DIM immigrants qui ont terminé un programme de formation médicale postdoctorale (PFMP) au Canada, et ce, afin de décrire la proportion -et les indicateurs correspondants -de personnes qui travaillent au Canada et dans les collectivités rurales canadiennes, en 2015.Méthode : Nous avons effectué un croisement entre la Base de données nationale sur les DIM et la Base de données médicales Scott' s afin de localiser les lieux de travail des CEE et des DIM immigrants en 2015.Résultats : Parmi les 1 214 DIM qui ont participé à un PFMP entre 2005 et 2011, la plupart travaillaient au Canada en 2015 (88,0 %).Relativement peu de DIM travaillaient dans des collectivités rurales (9,1 %).On ne remarque aucune différence dans les schémas de lieu de travail entre les CEE et les DIM immigrants.Conclusion : Contrairement à ce que les défenseurs des CEE suggèrent, les CEE connaissent les mêmes schémas de rétention du personnel que les DIM immigrants.Les politiques d' admission aux PFMP devraient traiter tous les DIM de la même façon, sans égard à leur citoyenneté ou à leur programme de résidence avant l'école de médecine.T

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.992
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 source (direct Gemma or distilled Codex), 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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