Brain drain in Türkiye’s nursing workforce: A literature review
Bibliographic record
Abstract
Nurses represent the largest workforce group forming the foundation of global health systems. Despite this central role, a critical worldwide nursing shortage persists, posing significant threats to the sustainability of health systems, particularly in low- and middle-income countries. Although Türkiye’s nursing workforce has expanded in recent years, it remains far below OECD (Organization for Economic Co-operation and Development) averages and struggles to meet the growing demand for healthcare services. This review adopts a comprehensive approach to examine the economic, organizational, social, and psychological factors accelerating nurse brain drain from Türkiye, drawing on national and international data. Findings indicate that low wages, heavy workloads, insufficient staffing, workplace violence, limited career opportunities, and burnout serve as major push factors influencing nurses’ decision to migrate. Conversely, high-income countries such as Germany, the Netherlands, Canada, and the United Kingdom offer strong pull factors including higher salaries, safer working environments, lower nurse-to-patient ratios, and well-developed professional career pathways. While Türkiye’s migration patterns share similarities with nurse-exporting countries such as the Philippines and India, high rates of workplace violence and the emigration of experienced nurses place Türkiye in a distinct position. Nurse brain drain has immediate consequences, including increased workload and negative impacts on patient safety, and long-term effects such as loss of institutional memory and transfer of educational investments to receiving countries. This review underscores the need for policy development addressing the structural drivers of nurse brain drain and provides a critical situational analysis for the future of Türkiye’s healthcare system.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".