Abroad Migration Intention among Nursing Students of Selected Nursing Colleges of the Kaski District
Bibliographic record
Abstract
Background: Nepal has a long history of nurses migrating abroad, with an estimated 10,000 nurses leaving the country annually. The United Kingdom, the United States of America, Australia, and Canada are the primary destinations for Nepali nurses seeking better economic opportunities and quality of life. This study aims to explore the intention of nursing students enrolled in pre-registered nursing programs in selected colleges in Kaski District to migrate abroad for employment. Methods: The study adopted a descriptive cross-sectional research design and selected four colleges out of seven using a simple random sampling technique. The study included only PCL nursing 3rd year and B.Sc. nursing 4th year students, with a total of 164 nursing students enrolled through simple random sampling. Data was collected using a self-developed, structured questionnaire and analyzed using SPSS (Statistical Package for the Social Sciences), version 16. Both descriptive and inferential statistics were used to analyze the data in accordance with the study's objectives. Results: This study found that the mean age of the respondents was 20.22±2.04 years, with 95.7% of them being female. Additionally, 93.3% of the respondents expressed an intention to migrate abroad, with only 6.7% of nurses not willing to do so. Furthermore, 30.1% of the respondents preferred Australia as their destination. The study also revealed that a majority of respondents from low socioeconomic backgrounds were planning to migrate abroad, with 54.9% citing responsibility towards family as a major reason for their migration preference. The study also found a statistically significant relationship between intention to migrate abroad and marital status (p=0.021) as well as monthly family income (p=0.028). Conclusions: The study found that a large majority of nursing students expressed a desire to migrate, with Australia being the most popular destination. The main reasons for this intention were a sense of responsibility towards their families and the prospect of higher salaries. Additionally, the study revealed that nursing students who were younger, unmarried, and from lower socioeconomic backgrounds were more likely to have intentions of migrating abroad.
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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 | 0.001 |
| 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) | 0.002 | 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".