Nursing professionalism and associated factors in Ethiopia: a systematic review and meta-analysis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».