Educational Objectives for International Medical Electives
Bibliographic record
Abstract
PURPOSE: Although most medical schools and residency programs offer international medical electives (IMEs), little guidance on the educational objectives for these rotations exists; thus, the authors reviewed the literature to compile and categorize a comprehensive set of educational objectives for IMEs. METHOD: In February and July 2012, the authors searched SciVerse Scopus online, which includes the Embase and MEDLINE databases, using specified terms. From the articles that met their inclusion criteria, they extracted the educational objectives of IMEs and sorted them into preelective, intraelective, and postelective objectives. RESULTS: The authors identified and reviewed 255 articles, 11 (4%) of which described 22 educational objectives. Among those 22 objectives, 5 (23%), 15 (68%), and 2 (9%) were, respectively, preelective, intraelective, and postelective objectives. Among preelective objectives, only cultural awareness appeared in more than 2 articles (3/11; 27%). Among intraelective objectives, the most commonly defined were enhancing clinical skills and understanding different health care systems (9/11; 82%). Learning to manage diseases rarely seen at home and increasing cultural awareness appeared in nearly half (5/11; 45%) of all articles. Among postelective objectives, reflecting on experiences through a written project was most common (9/11; 82%). CONCLUSIONS: The authors identified 22 educational objectives for IMEs in the published literature, some of which were consistent across institutions. These consistencies, in conjunction with future research, can be used as a framework on which institutions can build their own IME curricula, ultimately helping to ensure that their medical trainees have a meaningful learning experience while abroad.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".