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Record W2307680661

Predicting international medical graduate success on college certification examinations: responding to the Thomson and Cohl judicial report on IMG selection.

2014· article· en· W2307680661 on OpenAlexaffabout
Inge Schabort, Mathew Mercuri, Lawrence Grierson

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsMcMaster University
Fundersnot available
KeywordsIMGCertificationMedical educationMedicineGraduate medical educationFamily medicineAccreditationPolitical scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine predictors of international medical graduate (IMG) success in accordance with the priorities highlighted by the Thomson and Cohl judicial report on IMG selection. DESIGN: Retrospective assessment using regression analyses to compare the information available at the time of resident selection with those trainees' national certification examination outcomes. SETTING: McMaster University in Hamilton, Ont. PARTICIPANTS: McMaster University IMG residents who completed the program between 2005 and 2011. MAIN OUTCOME MEASURES: Associations between IMG professional experience or demographic characteristics and examination outcomes. RESULTS: The analyses revealed that country of study and performance on the Medical Council of Canada Evaluating Examination are among the predictors of performance on the College of Family Physicians of Canada and the Royal College of Physicians and Surgeons of Canada certification examinations. Of interest, the analyses also suggest discipline-specific relationships between previous professional experience and examination success. CONCLUSION: This work presents a useful technique for further improving our understanding of the performance of IMGs on certification examinations in North America, encourages similar interinstitutional analyses, and provides a foundation for the development of tools to assist with IMG education.

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)0.006
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.010
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.090
GPT teacher head0.413
Teacher spread0.322 · 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

Citations13
Published2014
Admission routes2
Has abstractyes

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