Geographical Distribution and Career Preferences of Nepali Medical Graduates- A Cross Sectional Study
Bibliographic record
Abstract
Nepal continues to face problems with the migration of its medical graduates both abroad and into major urban centers like Kathmandu Valley, worsening the already uneven distribution of doctors across the country. Despite years of expanding medical education with the goal of improving rural healthcare access, many graduates choose opportunities overseas or in cities, leaving rural and remote areas underserved. Understanding where Nepali medical graduates are practicing, and why, is essential for developing effective strategies to retain them and strengthen Nepal’s healthcare system. A descriptive cross-sectional survey was conducted from June 2023 to January 2025 among 1,208 Nepali medical graduates (both undergraduate and postgraduate levels) using a self-constructed electronic questionnaire. Participants were recruited through online platforms and alumni networks across all medical colleges in Nepal. Among the 1,208 respondents, 75.3% were male and 24.7% female, with a steady rise in female representation over time from 8.1% before 2000 to 37.4% after 2020. The majority (67.8%) graduated from Tribhuvan University. Overall, 66.8% were practicing in Nepal, while 33.2% worked abroad, predominantly in the United States (74.31% of those abroad). Within Nepal, 62.3% of graduates were working in Kathmandu Valley, with Bagmati Province hosting the highest number of doctors. Male graduates were more likely than females to work outside Kathmandu Valley. A significant association was found between graduation year and current practice location (p <0.0001), with post-2020 graduates showing the highest retention in Nepal. Gender disparities, university affiliation, and graduation year significantly influenced practice location. Addressing these challenges will require targeted policies that improve rural incentives, expand postgraduate opportunities outside urban centers, and create supportive, gender-sensitive work environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 teacher head, 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".