Too Many Doctors? Foreign Medical Graduates and the Debate over Health Care Accessibility in Canada, c.1976–1991
Bibliographic record
Abstract
Although the last decade has been dominated by commentators lamenting the national shortage of medical practitioners, only a generation ago policy makers concluded that most Canadian provinces had too many doctors. As a consequence, provincial ministers of health placed new restrictions on the licensing of foreign-trained health professionals. Assisted by the 1976 Immigration Act, Canada suddenly witnessed a precipitous drop in the number of newly licensed, foreign-trained doctors, a dramatic reversal of the previous decade which had seen over 10,000 physicians immigrate and take up practice in this country. The 1980s was notable for a variety of health care initiatives aimed at relocating the diminishing number of foreign physicians to underserviced areas, some of which were struck down as contravening the new Charter of Rights and Freedoms. This paper will examine the period of 1976-91, when, after two decades of relatively liberal immigration and licencing policies, Canadian provinces introduced new measures to restrict the scope of practice of incoming foreign-trained doctors and to divert them to underserviced areas. The paper will explore these health policy debates in order to understand better the context of the landmark Barer-Stoddart Report (1991), which concluded that a new interprofessional mix of health care practitioners was needed to reform, and make more accessible, the Canadian health care system.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.039 | 0.023 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".