International medical graduates (IMGs) needs assessment study: comparison between current IMG trainees and program directors
Bibliographic record
Abstract
BACKGROUND: International Medical Graduates (IMGs) training within the Canadian medical education system face unique difficulties. The purpose of this study was to explore the challenges IMGs encounter from the perspective of trainees and their Program Directors. METHODS: Program Directors of residency programs and IMGs at the University of Toronto were anonymously surveyed and asked to rate (using a 5-point Likert scale; 1 = least important - 5 = most important) the extent to which specific issues were challenging to IMGs and whether an orientation program (in the form of a horizontal curriculum) should be implemented for incoming IMGs prior to starting their residency. RESULTS: Among the IMGs surveyed, Knowledge of the Canadian Healthcare System received the highest mean score (3.93), followed by Knowledge of Pharmaceuticals and Hospital formularies (3.69), and Knowledge of the Hospital System (3.69). In contrast, Program Directors felt that Communication with Patients (4.40) was a main challenge faced by IMGs, followed by Communication with Team Members (4.33) and Basic Clinical Skills (4.28). CONCLUSION: IMGs and Program Directors differ in their perspectives as to what are considered challenges to foreign-trained physicians entering residency training. Both groups agree that an orientation program is necessary for incoming IMGs prior to starting their residency program.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".