Empirical impact evaluation of the WHO Global Code of Practice on the International Recruitment of Health Personnel in Australia, Canada, UK and USA
Bibliographic record
Abstract
BACKGROUND: The active recruitment of health workers from developing countries to developed countries has become a major threat to global health. In an effort to manage this migration, the 63rd World Health Assembly adopted the World Health Organization (WHO) Global Code of Practice on the International Recruitment of Health Personnel in May 2010. While the Code has been lauded as the first globally-applicable regulatory framework for health worker recruitment, its impact has yet to be evaluated. We offer the first empirical evaluation of the Code's impact on national and sub-national actors in Australia, Canada, United Kingdom and United States of America, which are the English-speaking developed countries with the greatest number of migrant health workers. METHODS: 42 key informants from across government, civil society and private sectors were surveyed to measure their awareness of the Code, knowledge of specific changes resulting from it, overall opinion on the effectiveness of non-binding codes, and suggestions to improve this Code's implementation. RESULTS: 60% of respondents believed their colleagues were not aware of the Code, and 93% reported that no specific changes had been observed in their work as a result of the Code. 86% reported that the Code has not had any meaningful impact on policies, practices or regulations in their countries. CONCLUSIONS: This suggests a gap between awareness of the Code among stakeholders at global forums and the awareness and behaviour of national and sub-national actors. Advocacy and technical guidance for implementing the Code are needed to improve its impact on national decision-makers.
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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 | 0.109 | 0.292 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".