Impact of midwives’ emigration on maternity care services in Nigeria: A cross-sectional survey
Bibliographic record
Abstract
Background: One of the paramount hurdles to Nigeria's development in maternity-care service is the emigration of midwives to developed countries. However, midwives’ perspectives on the effects of the emigration of their colleagues’ to developed countries have yet to be investigated. This study examined the impact of midwives’ emigration on maternity care services and associated challenges in Nigeria. Methods: This study adopted a cross-sectional survey of 121 midwives and nurses at Adeoyo Maternity Teaching Hospital (AMTH), Ibadan Nigeria. Data were collected using a self-structured questionnaire. Results: 98.3% reported negative maternal health outcomes, 98.3% indicated midwives’ burnout, 93.4% reveal midwives’ low morale. Logistic regression analysis identified that the strongest challenge faced by care providers was lack of staff support (OR = 4.05, p = 0.001). Conclusion: The nursing implications of midwife emigration are far-reaching, affecting both maternity care service and remaining midwives. The increased workload reduces the quality of care and poses a high risk to maternal and neonatal outcomes. Addressing these challenges requires a multi-faceted approach, including workforce development, policy reforms, retention strategies. Despite ongoing challenges, maternity-care services can be strengthened through implementation of these strategies.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".