Nurses’ Brain Drain in Pakistan: The Determinants and Reasons Pertaining to Nurses Leaving Pakistan to Overseas Territories in Health System
Bibliographic record
Abstract
The study examines the brain drain phenomenon among Pakistani nurses, focusing on the factors influencing their migration to countries like Kuwait, Canada, and the USA. A quantitative, cross-sectional study design was employed with survey data from 100 respondents, analyzing data from migrating nurses using paired sample t-tests. Results indicate that push and pull factors account for 65.67% and 83.32% of nurse migration. The research identifies key push factors, such as low salaries, heavy workloads, and political instability, alongside pull factors, including higher remuneration, safer environments, and better career opportunities abroad. The findings reveal that migration is predominantly driven by experienced, well-educated female nurses in their early to mid-career stages. This exodus poses a critical challenge to Pakistan's healthcare system, exacerbating understaffing and compromising service delivery. The study emphasizes the need for systemic reforms, including improved working conditions, competitive salaries, and clear professional growth pathways, to retain nursing talent. Healthcare policymakers are required to consider various push and pull factors causing the brain drain of experienced nurses. However, future research agenda calls for more insightful qualitative design considering the local and foreign healthcare policies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".