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 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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".