Health workforce data needed to minimize inequities associated with health-worker migration
Bibliographic record
Abstract
Policy & practiceSince more than a million refugees entered Germany during the Syrian conflict, discussions about their integration in the labour market have included assessments of the challenges faced by health workers.11,12 The number of medical professionals from the Syrian Arab Republic now providing health-care services in Germany has increased to more than 5000, second after German-trained doctors.13 This situation has raised concern that more migrant health workers in high-income countries will further undermine the ability of low-income countries to respond to pandemic demands.14 The COVID-19 pandemic has also intensified the Abstract: A persistent challenge with health-worker migration is the inequities it creates.To minimize these inequities, systems of global governance of health-worker migration have arisen which include various global codes of practice, agreements and reporting requirements.Reporting that is rigorous, open and transparent, and subject to scrutiny from the public, researchers, civil society organizations and other interested stakeholders, is important.One element of these codes and agreements with perhaps the greatest potential to deal with the impact of health-worker migration is more robust planning of the health workforce to address the goal of self-sufficiency.Open platforms for data sharing enable engagement of the public and stakeholders with data on the distribution and national origin of health workers and reveal policy strengths and weaknesses related to health-workforce planning.We explore recent policies directed at reducing the inequities from health-worker migration.While many of the examples used focus on nurses and doctors, the issues discussed are relevant to all cadres of internationally trained health workers.
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 | 0.068 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".