Investigating the emigration intention of health care workers: A cross‐sectional study
Bibliographic record
Abstract
AIMS: To (1) explore the intramigration experience of HCWs within Nigeria, (2) explore the migration intention of health care workers (HCWs) in Nigeria and (3) identify the predictors of migration intention among HCWs in Nigeria. DESIGN: Cross-sectional study. METHODS: The online survey was used to collect data from 513 HCWs in Nigeria between May and June 2023. Crude and adjusted logistic regression were used to identify factors associated with emigration intention. Analyses were performed on SPSS version 26 at a 95% confidence interval. RESULTS: The study found that 34.4% had intramigration experience, and the rate of intention to emigrate to work in another country was 80.1%. The United Kingdom was the most preferred destination (109 HCWs), followed by Canada (92 HCWs) and the United States (82 HCWs). At the multivariate level, emigration intention was associated with the experience of burnout and duration of practice as a HCW. Nurses had higher emigration intentions than medical doctors. CONCLUSIONS: Many HCWs in Nigeria appear to have emigration intent, and nurses are more likely to be willing to migrate than doctors. The Nigerian government may want to explore strategies to reverse the emigration intent of the HCWs in Nigeria.
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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 | 0.003 |
| 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 | 0.001 |
| 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".