IMG candidates' demographic characteristics as predictors of CEHPEA CE1 results.
Bibliographic record
Abstract
OBJECTIVE: To assess the extent to which demographic characteristics are related to international medical graduate (IMG) candidate performance on the Centre for the Evaluation of Health Professionals Educated Abroad General Comprehensive Clinical Examination 1 (CE1). DESIGN: Retrospective study. SETTING: Toronto, Ont. PARTICIPANTS: All IMG candidates who registered for and took the CE1 in 2007 (n = 430), 2008 (n = 480), and 2009 (n = 472) were included in this analysis. All candidates completed the Centre for the Evaluation of Health Professionals Educated Abroad CE1, a 12-station objective structured clinical examination. MAIN OUTCOME MEASURES: Mean (SD) examination scores for groups based on demographic variables (age, region of medical training, and Medical Council of Canada Qualifying Examination Part 1 [MCCQE1] score) were calculated. Analysis of variance was done using CE1 examination total scores as the dependent variables. RESULTS: Candidates from countries where both medical education and patient care are conducted in English and those from South America and Western Europe achieved the highest scores, while candidates from the Western Pacific region and Africa achieved the lowest scores. Younger candidates achieved higher scores than older candidates. These results were consistent across the 3 years of CE1 examination administration. There was a significant relationship between MCCQE1 and CE1 scores in 2 of the 3 years: 2007 (r = 0.218, P < .001) and 2008 (r = 0.23, P < .01). CONCLUSION: The CE1 includes an assessment of communication skills; hence it is reasonable that candidates with stronger English skills have the highest scores on the CE1. Age, as a proxy for time since graduation, also has a substantial effect on examination scores, possibly owing to those further from their training lacking some currency of knowledge or being in focused rather than general practices. It is reasonable that those who had higher scores on the written test (the MCCQE1) would also have higher scores on the clinical test (the CE1). Demographic characteristics appear to be related to performance on the CE1.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".