Migration Intentions, Practice Environment, and Satisfaction among Nigerian Nurses: A Case Study
Bibliographic record
Abstract
Background: Nursing workforce migration is a function of the nursing practice environment and satisfaction with the general situation of their country of practice. There is a need to provide empirical data on the intent to migrate among nurses and satisfaction with the working environment in Nigeria.Purpose: This study assessed migration intention, favorability of practice environment, and level of satisfaction with the Nigerian environment among nurses in a private teaching hospital in Nigeria.Methods: This descriptive cross-sectional study recruited participants using a simple random sampling technique. In all, 124 nurses participated in the study. Data was collected using the migration intention questionnaire, nursing practice environment scale, and satisfaction with Nigeria environment questionnaire. All ethical principles were adhered to. Data was analyzed using Statistical Package for Social Sciences. Descriptive statistics (frequency, mean and standard deviation) were used. Results: An overwhelming majority (95.2%) have the intention to migrate to other countries with 63.6% of them already in the migration process. Canada (34.8%) and the United Kingdom (33.9%) were the most sought-after countries. Nurse manager ability, leadership, and support scored highest on the favourability of the nursing practice environment (2.92±0.80) while staffing and resources inadequacy has the lowest score (2.63±0.68). Overall, 75.8% of the nurses describe their practice environment as favourable. Political conflicts and wars are the most dissatisfying areas of Nigeria's environment. Also, the majority 61.3% were dissatisfied with the Nigerian environment.Conclusion: The majority of the nurses who participated in the study are planning to migrate to another country. The majority of the nurses are not satisfied with Nigeria’s environment and they opined that their practice environment is unfavourable. There is a need to make the nursing practice environment more favorable to the nurses.
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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 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".