Do international medical graduates’ recruitment policies help to overcome healthcare shortage areas in developed countries? A systematic review
Bibliographic record
Abstract
This review investigates the effectiveness of utilizing foreign physicians or International Medical Graduates to alleviate medical shortages in rural and underserved areas of developed countries. Conducted in February 2024, this systematic review follows PRISMA 2020 guidelines, analysing 15 English-language studies from the United States, Canada, Australia, and New Zealand. The focus is on comparing physicians with international graduation to national graduates in rural and underserved contexts. Results reveal diverse trends across countries: in the United States, national graduates are generally more represented in rural areas, while foreign physicians are more prevalent in Health Professional Shortage Areas. In Canada, foreign graduates are more common in rural areas, varying by province. Australia and New Zealand show foreign physicians practicing more in rural areas than national counterparts. This study underscores significant reliance on foreign physicians to mitigate rural healthcare disparities. While this strategy partially addresses immediate shortages, long-term effectiveness is uncertain due to retention and integration challenges. Future policies should focus on sustainable solutions for equitable healthcare access and physicians' retention in underserved areas. This review emphasizes also the need for Europe-specific studies and further evaluation of policy effectiveness.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".