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Record W2314378292 · doi:10.1097/acm.0b013e318222e314

Comparison of Canadian Medical Graduates and International Medical Graduates in Canada: 1989–2007

2011· article· en· W2314378292 on OpenAlexaffabout
Philip S. Mok, Mark O. Baerlocher, Caroline Abrahams, Eva Y.L. Tan, Steve Slade, Sarita Verma

Bibliographic record

VenueAcademic Medicine · 2011
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSpecialtyMedicineFamily medicineHealth careDemographyPolitical science

Abstract

fetched live from OpenAlex

PURPOSE: To compare Canadian medical graduates (CMGs) and international medical graduates (IMGs) who completed postgraduate medical education in Canada from 1989 to 2007 by age, gender, specialty, and practice characteristics. METHOD: Data on all CMGs and IMGs who completed residencies or fellowships in Canada from 1989 to 2007 were extracted from the Canadian Post-M.D. Education Registry. Data from 1989-1993 and 2003-2007 were pooled for analysis. RESULTS: A total of 8,501 CMGs and 1,828 IMGs completed post-MD training at Canadian institutions between 1989 and 1993 inclusive; 7,734 CMGs and 1,879 IMGs completed such training between 2003 and 2007. From 1989-1993 to 2003-2007, the average age of CMGs increased from 29.8 to 31.1 years, and average age of IMGs increased from 36.1 to 37.0 years. From 1989-1993 to 2003-2007, the percentage of women increased from 41% (3,471/8,501) to 52% (4,016/7,734) and from 28% (509/1,828) to 42% (791/1,879) for CMGs and IMGs, respectively. The proportion of CMGs who trained in family medicine declined from 54% (4,568/8,501) to 38% (2,921/7,734) from 1989-1993 to 2003-2007. The percentage of IMGs who trained in family medicine increased from 19% (344/1,828) to 37% (699/1,879) during the same period. CONCLUSIONS: IMGs tended to be older, more likely to be men, and more likely to pursue family medicine than their CMG counterparts. These differences have implications in designing future health care policy and recruiting physicians from abroad. Other countries could look at their own physician demographics using this study's methods.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0120.000

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 head0.169
GPT teacher head0.469
Teacher spread0.300 · 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
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

Citations15
Published2011
Admission routes2
Has abstractyes

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