Validation of a large-scale clinical examination for international medical graduates.
Bibliographic record
Abstract
OBJECTIVE: To evaluate a new examination process for international medical graduates (IMGs) to ensure that it is able to reliably assign candidates to 1 of 4 competency levels, and to determine if a global rating scale can accurately stratify examinees into 4 levels of learners: clerks, first-year residents, second-year residents, or practice ready. DESIGN: Validation study evaluating a 12-station objective structured clinical examination. SETTING: Ontario. PARTICIPANTS: A total of 846 IMGs, and an additional 63 randomly selected volunteers from 2 groups: third-year clinical clerks (n = 42) and first-year family medicine residents (n = 21). MAIN OUTCOME MEASURES: The accuracy of the stratification of the examinees into learner levels, the impact of the patient-encounter ratings and postencounter oral questions, and between-group differences in total score. RESULTS: Reliability of the patient-encounter scores, postencounter oral question scores, and the total between-group difference scores was 0.93, 0.88, and 0.76, respectively. Third-year clerks scored the lowest, followed by the IMGs. First-year residents scored highest for all 3 scores. Analysis of variance demonstrated significant between-group differences for all 3 scores (P < .05). Postencounter oral question scores differentiated among all 3 groups. CONCLUSION: Clinical examination scores were capable of differentiating among the 3 groups. As a group, the IMGs seemed to be less competent than the first-year family medicine residents and more competent than the third-year clerks. The scores generated by the postencounter oral questions were the most effective in differentiating between the 2 training levels and among the 3 groups of test takers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".