Predictors for success and failure in international medical graduates: a systematic review of prognostic factor studies
Bibliographic record
Abstract
Introduction International Medical Graduates (IMG) are an essential part of the international physician workforce, and exploring the predictors of success and failure for IMGs could help inform international and national physician labour workforce selection and planning. Method We searched 11 databases, including Medline, Embase and LILACS, from inception to February 2022 for studies that explored the predictors of success and failure in IMGs. We reported baseline probability, effect size in relative risk (RR), odds ratio (OR) or hazard ratio (HR) and absolute probability change for success and failure across six groups of outcomes, including success in qualifying and certificate exams, successful matching into residency, retention in practice, disciplinary actions, and outcomes of IMG clinical practice. Result Twenty-five studies (375,549 participants) reported the association of 93 predictors of success and failure for IMGs. Female sex, English proficiency, graduation recency, higher scores in USMLE step 2 and participation in a skill assessment program were associated with success in qualifying exams. Female sex, fluency in English, previous internship and results of qualifying exams were associated with success in certification exams. Retention to work in Canada was associated with several factors, including male gender, graduating within the past five years, and completing residency over fellowships. In the UK, IMGs and candidates who attempted PLAB part 1, ≥4 times vs first attempters, and candidates who attempted PLAB part 2, ≥3 times vs first attempters were more likely to be censured in future practice. Patients treated by IMGs had significantly lower mortalities than those treated by US graduates, and patients of IMGs had lower mortalities [OR: 0.82 (95% CI: 0.62, 0.99)] than patients of US citizens who trained abroad. Conclusion This study informed factors associated with the success and failure of IMGs and is the first systematic review on this topic, which can inform IMG selection and future studies.
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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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".