Nursing professionalism and associated factors in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background A higher level of nursing professionalism improves autonomy among nurses, the quality of nursing care, and patient outcomes. However, inconsistent findings on the prevalence of nursing professionalism and associated factors have been reported among studies conducted in Ethiopia, and a meta-analysis of pooled results have not been performed. Therefore, the aim of this systematic review and meta-analysis was to determine the pooled prevalence of higher levels of nursing professionalism and factors associated with it. Methods PubMed, Science Direct, HINARI, African Journals Online, Google Scholar, and university online institutional repositories in Ethiopia were accessed from 15/10/2024–30/10/2024. The items were assessed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The quality of the included studies was assessed via the Newcastle–Ottawa Scale. Cross-sectional studies were included without time period limits. Data extraction was conducted via Microsoft Excel and analyzed with STATA 17. The Galbraith plot, I 2 statistic and meta-regression were used to determine heterogeneity. We used a random effects model in the presence of heterogeneity. Publication bias was assessed via funnel plots and Egger’s based regression. We also computed a sensitivity analysis and subgroup analysis by sample size and study period. Results Twelve primary studies involving 3710 nurses were included in this systematic review and meta-analysis. The pooled prevalence of higher levels of nursing professionalism was 43%. Bachelor's degree and above educational status (POR: 1.80, CI: 1.38, 2.33), learning from government colleges (POR: 2.14, CI: 1.34, 3.42), better payment (POR: 1.85, CI: 1.16, 2.98), long years of work experience (POR: 2.15, CI: 1.73, 2.68), positive self-image (POR: 3.85, CI: 2.17, 6.84), job satisfaction (POR: 2.42, CI: 1.49, 3.95) and training opportunities (POR: 2.88, CI: 1.14, 7.32) were factors that determined higher levels of nursing professionalism in Ethiopia. Conclusion The pooled prevalence of higher levels of nursing professionalism in Ethiopia was low. Educational status, and attending college, payment, work experience, self-image, job satisfaction, and training were factors that determined the level of professionalism. These factors can be modified to increase the level of nursing professionalism in Ethiopia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".