Migration intentions among nursing students in a low-middle-income country
Bibliographic record
Abstract
BACKGROUND: Migration among skilled labour has been noted as one of the major issues in recent times, especially among health workers. Data from the United Nations show that almost two thirds of people migrating are labor migrants and international migrants constitute 3.5% of the global migration population. Out of the millions of people who migrate across the globe, health workers, especially nurses form a greater portion of these numbers. This study explored nursing students' intention to migrate to other countries after completing their programs. METHOD: A descriptive cross-sectional design approach was adopted using self-administered questionnaire that contain aspects of open-ended questions. A sample size of 226 nursing students were recruited using convenient sampling technique. RESULTS: The results overall, revealed that 226 nursing students participated in the study. Out of this, most of the respondents 42.5% were aged between 25 and 30 years with majority 53.1% being males. Also, 35% of the participants were married with more than half 59.7% of the respondents being Christians. The results further revealed that most of the participants 64.2% had intention of migrating to other countries. Among those who intended to migrate, 11.7% identified lack of jobs, 39.3% identified low salaries in Ghana while 50.3% identified bad working conditions. The rest 2.8% attributed their intentions to migrate to educational opportunities. Common places of destination included Canada, USA, UK and Australia. CONCLUSION: The outcome of this study points to the urgent need for low-income countries such as Ghana to urgently put in measures to curb the menace of brain drain among nurses. Improvement in working condition of nurses must be prioritized to motivate their stay.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".