The International Migration of Doctors: Impacts and Political Implications
Bibliographic record
Abstract
If the international migration of doctors has been part of the brain drain debate, few studies have focused on the question in depth due to statistical data limitations. An innovative data source based on foreign-trained doctors over the period 1991 to 2004, made it possible to draw up an overview of the migration flow of doctors, to study its impact and draw economic policy implications.The Asian countries record the highest emigration rates for doctors (India, the Philippines), followed by Canada and the United Kingdom with France in 25th position. In 2004, Subsaharan Africa recorded the lowest density of doctors in the world but a relatively high emigration rate at 19%. In 2004, 60% of foreign-trained doctors were located in the United States, the country receiving the highest number of doctors in the world, and 20% in the United Kingdom. Australia, Canada and Germany each receive 3%, Belgium 2% and France 1.34%.What effect do these migrations have on the origin countries both from an economic point of view and in terms of health indicators? What lines of action or public policies can be envisaged in the face of emigration? What form of international cooperation can be envisaged in terms of health professionals’ international mobility? What are the impacts on the receiving countries’ in terms of health profession regulation policies?
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".