The 'Brain Drain' of Physicians: Historical antecedents to an ethical debate, c. 1960-79
Bibliographic record
Abstract
Many western industrialized countries are currently suffering from a crisis in health human resources, one that involves a debate over the recruitment and licensing of foreign-trained doctors and nurses. The intense public policy interest in foreign-trained medical personnel, however, is not new. During the 1960s, western countries revised their immigration policies to focus on highly-trained professionals. During the following decade, hundreds of thousands of health care practitioners migrated from poorer jurisdictions to western industrialized countries to solve what were then deemed to be national doctor and nursing 'shortages' in the developed world. Migration plummeted in the 1980s and 1990s only to re-emerge in the last decade as an important debate in global health care policy and ethics. This paper will examine the historical antecedents to this ethical debate. It will trace the early articulation of the idea of a 'brain drain', one that emerged from the loss of NHS doctors to other western jurisdictions in the 1950s and 1960s. Only over time did the discussion turn to the 'manpower' losses of 'third world countries', but the inability to track physician migration, amongst other variables, muted any concerted ethical debate. By contrast, the last decade's literature has witnessed a dramatically different ethical framework, informed by globalization, the rise of South Africa as a source donor country, and the ongoing catastrophe of the AIDS epidemic. Unlike the literature of the early 1970s, recent scholarship has focussed on a new framework of global ethics.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.088 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".