Australia operates "closed shop" to restrict doctors from overseas, say critics
Bibliographic record
Abstract
Overseas trained doctors seeking to work in Australia often face unwarranted restrictions on practising, say leading Australian medical specialists and healthcare reform advocates. Ian Hickie, a psychiatrist at the University of Sydney, says that shortages in the medical workforce are being exacerbated by restrictions caused by an “evil axis” of immigration policy, health regulations, and the monopoly of specialist medical colleges over training and accreditation. Australia was allowing a “closed shop” to control its medical workforce in a way that would not be tolerated in any other industry, he said. Concerns about quality and safety were often used as a “smokescreen” to maintain the position of local graduates, he added. “We’re quite happy to have all these overseas trained doctors work in our system, so long as they don’t exercise the same economic and civil rights [as Australian graduates],” said Professor Hickie. The recently publicised case of a Canadian doctor who has been unable to gain full rights of practice (www.smh.com.au/national/a-bitter-pill-to-swallow-when-a-doctor-feels-doublecrossed-20091012-gtyx.html) showed that workforce reform should be a major focus of the current national push for healthcare reform, he said. Susan Douglas, who moved to Australia in 2006 to take up an appointment as senior lecturer in general practice at the Australian National University, …
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".