The intention of migration and associated factors among nursing undergraduates of selected state universities in Sri Lanka
Bibliographic record
Abstract
Migration for better career opportunities and quality of life is a global trend. Due to the increasing demand for nurses in developed countries, Sri Lankan nursing undergraduates seek professional and personal growth abroad, driven by challenges in their home-country, such as limited career advancement and low salaries. The objective of this study was to identify the intention of migration (ITM) and its associated factors among nursing undergraduates in selected state universities in Sri Lanka. A descriptive cross-sectional study was conducted at two state universities in the Western and Southern Provinces of Sri Lanka. A total of 384 nursing undergraduates were selected through stratified random sampling. A validated, pre-tested, self-administered questionnaire adapted from a previous study was used to measure ITM using five statements on a 5-point Likert scale. Data were analyzed using SPSS v.26. ITM was categorized into “high” and “low” based on the median score cutoff value. The chi-square test was used to identify the associations between ITM levels and associated factors. Ethical approval was obtained from the Ethics Review Committee of the Faculty of Arts, University of Colombo. In total, 300 (78.12%) participants responded. Nearly half (52%) of nursing undergraduates had a high ITM, with Australia being the most preferred destination (n=201), followed by the UK (n=187) and Canada (n=151). Factors influencing ITM included gender, marital status, birth location, family economy, overseas experiences, having relatives/friends abroad, and future career plans (p<0.05). Females, urban undergraduates, and undergraduates from higher socio-economic backgrounds were more likely to express a higher ITM. A considerable proportion of nursing undergraduates have a high ITM, and the complex interplay of socio-economic and personal factors influences ITM. Marital status, birth location, family economy, overseas experiences, having relatives/friends abroad, and future career plans were the major determinants of ITM. Government policies need to be established to facilitate ethical migration by balancing the positive and negative impacts of nurses’ migration to the country.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".