Relevance and Effectiveness of the WHO Global Code Practice on the International Recruitment of Health Personnel – Ethical and Systems Perspectives
Bibliographic record
Abstract
The relevance and effectiveness of the World Health Organization's (WHO's) Global Code of Practice on the International Recruitment of Health Personnel is being reviewed in 2015. The Code, which is a set of ethical norms and principles adopted by the World Health Assembly (WHA) in 2010, urges members states to train and retain the health personnel they need, thereby limiting demand for international migration, especially from the under-staffed health systems in low- and middle-income countries. Most countries failed to submit a first report in 2012 on implementation of the Code, including those source countries whose health systems are most under threat from the recruitment of their doctors and nurses, often to work in 4 major destination countries: the United States, United Kingdom, Canada and Australia. Political commitment by source country Ministers of Health needs to have been achieved at the May 2015 WHA to ensure better reporting by these countries on Code implementation for it to be effective. This paper uses ethics and health systems perspectives to analyse some of the drivers of international recruitment. The balance of competing ethics principles, which are contained in the Code's articles, reflects a tension that was evident during the drafting of the Code between 2007 and 2010. In 2007-2008, the right of health personnel to migrate was seen as a preeminent principle by US representatives on the Global Council which co-drafted the Code. Consensus on how to balance competing ethical principles--giving due recognition on the one hand to the obligations of health workers to the countries that trained them and the need for distributive justice given the global inequities of health workforce distribution in relation to need, and the right to migrate on the other hand--was only possible after President Obama took office in January 2009. It is in the interests of all countries to implement the Global Code and not just those that are losing their health personnel through international recruitment, given that it calls on all member states "to educate, retain and sustain a health workforce that is appropriate for their (need) ..." (Article 5.4), to ensure health systems' sustainability. However, in some wealthy destination countries, this means tackling national inequities and poorly designed health workforce strategies that result in foreign-trained doctors being recruited to work among disadvantaged populations and in primary care settings, allowing domestically trained doctors work in more attractive hospital settings.
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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 | 0.407 | 0.331 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.012 | 0.073 |
| Scholarly communication | 0.034 | 0.021 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.012 | 0.011 |
| 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".