About half of Ethiopian midwifery professionals reported being dissatisfied with their jobs: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Increasing well qualified health professionals is a part of sustainable development goal to specially to decrease maternal mortality below 70 per 100,000 deaths. Contrarily, The Nursing and midwifery councils (NMC) expect that 36% of healthcare workers, especially midwives, are leaving their jobs due to high turnover rates and job unhappiness worldwide. Methods: Studies were rigorously searched utilizing international databases from PubMed, Google Scholar, Cochrane Library, and Embase. Using the New Castle Ottawa scale for a cross-sectional study design, the quality of the articles that were searched was evaluated. The systemic review was conducted using the random effect approach, and statistical analysis was done using STATA version 17 software for the window. The Preferred Reporting Item for Systematic Review and Meta-Analyses (PRISMA) guideline was followed for reporting results. Results: A total of nine observational cross-sectional studies were included in this review. The pooled level of job satisfaction among midwives in Ethiopia was 52.2% (95% CI =41.7, 62.9). The pooled odds ratio showed that a significant positive association was found between midwives' job satisfaction and studied variables. Male midwife (OR = 0.45; 95% CI: 0.04, 0.87), fair supervision (OR = 2.03; 95%CI: 1.58-1), workload (OR = 1.72; 95%CI: 1.102-2.43) and motivation (OR = 1.64; 95%CI: 1.02-2.25) were strongly associated with job satisfaction. Conclusion: Evidence suggested that motivating employees, providing fair supervision, fair workloads, and fostering positive relationships with managers are all crucial tactics for retaining and enhancing the satisfaction of health professionals at health care facilities in Ethiopian.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".