Intention, Motivations, and Barriers to Emigration of Nursing Students in Colombia
Bibliographic record
Abstract
Objective. To explore the intention, motivations, and barriers to emigrate of final semester nursing students from Colombia. Methods. Quantitative, descriptive, and cross-sectional study with participation by 556 last-semester students matriculated in 26 undergraduate nursing programs in Colombia. Data were collected through an online questionnaire. Results. The study found that 84% of the participants consider among their plans as future nursing professionals to emigrate to practice their profession in another country. Destinations of preference for those who have thought of emigrating include countries, like Canada (63.5%), Spain (57.7%), Germany (44.9%), and the United States (44.4%). The main reasons that motivate nursing students to emigrate when they complete their professional studies are: better remuneration (81.6%), better quality of life (67.9%), greater professional growth (64.1%), greater job stability (54.7%), and more employment options (49.8%). In turn, the reasons that discourage nursing students from emigrating when they complete their professional studies are: language (71.9%), going away from the family (60.6%), and the complexity of the emigration process (55.4%). Conclusion. The findings of this research show that a notable proportion of last-semester nursing students consider among their plans to emigrate to practice in another country when they receive their degree. Knowing the intentions, motivations, and barriers to emigrate of future nurses will permit having elements to design strategies that improve the retention of professionals in Colombia.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".