Research Trends and Patterns on International Migration of Health Workers (1950–2022)
Bibliographic record
Abstract
In the context of globalization and liberalization, the international migration of health workers has gained prominence due to increased cross-border mobility. This migration trend, impacting healthcare systems, has intensified recently, driven by factors like the COVID-19 pandemic and demand in high-income countries. The present study was designed to give an overview on research trends and patterns of scholarly production on this topic. Relevant documents published in Scopus from 1950 to 2022 were extracted and analyzed using bibliometric methods. The search string extracted 708 articles. The Human Resources for Health journal ranked first while the University of Sydney was the leading institution. Authors from the US contributed the most (25.8%), followed by the UK, Canada, and Australia. At the regional level, countries in the WHO South-Eastern Asian region and WHO Eastern Mediterranean region contributed the least. Cross-country research collaboration was limited. The research hotspots that attracted the attention of scholars were nurse and physician migration, policy implications, and impact on developing countries. Emerging research topics were the impact of COVID-19 on migration and identification of push-pull factors. Most frequently mentioned push factors driving health worker migration included economic disparities, unfavorable work conditions, and security concerns. Conversely, the main pull factors encompassed prospects of advanced training, better quality of life, and enhanced practice environments. Future research should focus on global policies and push-pull factors to restore the balance in the ratio of health workforce between high and low- and middle-income countries.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.052 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".