Bibliographic record
Abstract
The past three decades have seen the number of international migrants double, to reach the unprecedented total of 175 million people in 2003. National health systems are often the biggest national employer, responsible for an estimated 35 million workers worldwide. Health professionals are part of the expanding global labour market. Today, foreign-educated health professionals represent more than a quarter of the medical and nursing workforces of Australia, Canada, the United Kingdom and the United States. Destination countries, however, are not limited to industrialised nations. For example, 50 per cent of physicians in the Namibia public services are expatriates and South Africa continues to recruit close to 80% of its rural physicians from other countries. International migration often imitates patterns of internal migration. The exodus from rural to urban areas, from lower to higher income urban neighbourhoods and from lower-income to higher-income sectors contributes challenges to the universal coverage of the population. International migration is often blamed for the dramatic health professional shortages witnessed in the developing countries. A recent OECD study, however, concludes that many registered nurses in South Africa (far exceeding the number that emigrate) are either inactive or unemployed. These dire situations constitute a modern paradox which is for the most part ignored. Shared language, promises of a better quality of life and globalization all support the continued existence of health professionals' international migration. The ethical dimension o this mobility is a sensitive issue that needs to be addressed. A major paradigm shift, however, is required in order to lessen the need to migrate rather than artificially curb the flows.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".