Health professional mobility and the Global Code of Practice: joint EUROSTAT/OECD/WHO survey data
Bibliographic record
Abstract
Abstract Background This study assesses the impact and continuing relevance of the Code of Practice on the International Recruitment of Health Personnel in the WHO Europe region by analysing trends over time in intra- and inter-regional health worker mobility. Methods Data from the joint EUROSTAT/OECD/WHO questionnaire are analysed to determine 1) the proportion of foreign-born and foreign-trained doctors and nurses working in WHO Europe Member States, 2) trends in health workforce mobility over time by country of origin and destination, and 3) how the Global Code has impacted mobility patterns. Results The size of the foreign-trained health workforce in Europe varies widely, with foreign-trained doctors comprising over a quarter of the workforce in Norway, Switzerland and the UK, but below 2% in Estonia and Serbia. While annual in-flows across the region have been stable since 2009, the share of foreign-trained doctors and nurses have both increased by over 30%. Mobility between The Commonwealth of Independent States has remained steady, but an increase in East-West and South-North migration is observed, driven by European Union expansion in 2004 and the economic crisis. Migration of health workers into Europe from developing countries covered by the Code has fluctuated, with increased numbers seen from some origin countries (e.g. Nigeria, Pakistan). Some Western countries remain reliant on a foreign-trained health workforce. This contributes to a high outward flow of health professionals from other European countries and creates challenges for sustainable workforce development. Conclusions The Global Code remains highly relevant, but other factors have more impact on migration flows, such as free movement in the EU. Health workforce mobility data can be improved to support a 'whole of workforce' approach to policy and planning by including more professional groups, and by adding qualitative indicators, e.g. individual perceptions and intention to leave.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".