Bibliographic record
Abstract
OBJECTIVES: The international migration of health care professionals has been recognized as a public health concern. A series of 'push' and 'pull' factors have been identified as driving forces for migration of doctors. The USA, UK, Canada and Australia are the main beneficiaries of medical migration, which has adverse consequences for health care systems in developing countries. Recently, a Global Code of Practice on the International Recruitment of Health Personnel was adopted by the World Health Assembly. In this paper, a summary of the most important recommendations of the Code is presented. In addition, the case of overseas trained psychiatrists in Australia is illustrated. These specialists complain of discriminatory practices due to the lack of recognition of their professional credentials. Research evidence from different countries confirms that international medical graduates face discriminatory obstacles to exercise their rights and practise their professions in developed countries. CONCLUSIONS: An international strategy is required to promote sustainable health care systems worldwide. Additional academic and scientific partnerships must be established between developed and developing nations in order to minimize discrepancies. There is an urgent need to review policies related to the recognition of medical credentials in host countries, including Australia. There are clear implications for psychiatry and psychiatrists.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".