Nonacademic Attributes Predict Medical and Nursing Student Intentions to Emigrate or to Work Rurally: An Eight-Country Survey in Asia and Africa
Bibliographic record
Abstract
AbstractWe sought to identify independent, nonacademic predictors of medical and nursing student intent to migrate abroad or from rural to urban areas after graduation in low- and middle-income countries (LMIC). This was a cross-sectional survey of 3,199 first- and final-year medical and nursing students at 16 training institutions in eight LMIC. Questionnaires assessed demographics, career intentions, and preferences regarding selected career, location, and work-related attributes. Using principal component analysis, student preferences were reduced into four discrete categories of priorities: 1) work environment resources, 2) location livability, 3) altruistic job values, and 4) individualistic job values. Students' preferences were scored in each category. Using students' characteristics and priority scores, multivariable proportional odds models were used to derive independent predictors of intentions to emigrate for work outside the country, or to work in a rural area in their native country. Students prioritizing individualistic values more often planned international careers (adjusted odds ratio [aOR] = 1.44, 95% confidence interval [CI] = 1.16-1.78), whereas those prioritizing altruistic values preferred rural careers (aOR = 1.82, 95% CI = 1.50-2.21). Trainees prioritizing high-resource environments preferentially planned careers abroad (aOR = 1.38, 95% CI = 1.12-1.69) and were unlikely to seek rural work (aOR = 0.60, 95% CI = 0.49-0.73). Independent of their priorities, students with prolonged prior rural residence were unlikely to plan emigration (aOR = 0.67, 95% CI = 0.50-0.90) and were more likely to plan a rural career (aOR = 1.53, 95% CI = 1.16-2.03). We conclude that use of nonacademic attributes in medical and nursing admissions processes would likely increase retention in high-need rural areas and reduce emigration "brain drain" in LMIC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".