Expectativas de migración internacional en estudiantes de enfermería en México, Distrito Federal Migration expectations among nursing students in Mexico City
Bibliographic record
Abstract
OBJETIVO: Analizar los factores asociados con la expectativa de migrar al extranjero en estudiantes de licenciatura en enfermería de escuelas públicas en México, Distrito Federal. MATERIAL Y MÉTODOS: Se realizó un estudio transversal con una muestra no probabilística de 420 estudiantes. Se construyó un modelo logístico multivariado. RESULTADOS: El 69% de los informantes expresó la intención de migrar para trabajar (65%) o estudiar (26%). El 50% elegiría como destino Canadá, seguido de España y Estados Unidos. Las variables asociadas con la expectativa de migrar fueron: edad, ingreso, tener familiares en el extranjero y percibir malas condiciones laborales/salarios en México. CONCLUSIONES: Los resultados concuerdan con la literatura internacional. Los bajos salarios, malas condiciones laborales y escasas posibilidades de desarrollarse profesionalmente en México contribuyen a generar la expectativa de migrar en la población de estudio. Adicionalmente, la percepción optimista de los estudiantes sobre el mercado extranjero y la demanda laboral de países desarrollados coadyuvan a enfatizar dicho fenómeno. OBJECTIVE: To analyze the factors associated with the expectations to migrate abroad among nursing students in Mexico City. MATERIAL AND METHODS: A cross-sectional study was conducted with a non-random sample of 420 students. A logistic regression model was estimated. RESULTS: A total of 69% of the informants expressed their intention to move abroad, to look for employment (65%) and/or to continue their studies (26%). Of those, 50% would choose Canada as their destination, followed by Spain and the United States. The variables associated with migration expectations were: age, income, having relatives abroad, and perception of poor labor conditions and low wages in Mexico. CONCLUSIONS: Results are consistent with international literature. Low wages, poor labor conditions and the limited possibilities for professional development in Mexico are factors that contribute to generate migration expectations among nursing students. Additionally, optimistic perceptions about the job market and the labor demand in more developed countries heighten expectations to migrate.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| 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".