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Background: Nurses with reduced work ability had a high risk of disability pension, sickness absences, retirement intention, and leave their job and profession early. Nurses frequently suffer from job related stress, occupational fatigue and sleep problems, which can further compromise their work ability. Aims of the study: The aim of this study was to assess perceived work ability and its associated factors among nurses working in the Northwest of Amhara regional state Referral Hospitals, Northwest Ethiopia, 2022. Methods: A multicenter, an institutional based, cross-sectional study was conducted among 410 nurses working in five selected Referral hospitals, found in the Northwest of Amhara regional state, Northwest Ethiopia, 2022. The data were collected using a structured, self-administered questionnaire and entered using Epi info version 7.2.5 software, analyzed using SPSS version 25. Summary statistics (median or IQR for continuous data and frequency and percentage for categorical variables) were used. The ordinal logistic regression was used to assess' the presence of association between dependent and independent variables. Results: The findings of this study revealed that 59.0% of nurses had poor level of work ability, whereas 34.4% and 6.6% of nurses had sub-optimal and optimal level of work ability respectively. Multivariable ordinal logistic regression revealed that being male [AOR = 2.43; 95% CI (1.52, 3.91)], being BSC nurse [AOR = 0.21; 95% CI (0.08, 0.51)], nurses who had poor sleep quality [AOR = 0.34; 95% CI (0.12, 0.98)] and nurses who had chronic disease [AOR = 0.18; 95% CI (0.08, 0.41)] were significantly associated with nurses' level of work ability, p-value < 0.05. Conclusions: In this study, the prevalence of poor level of work ability among nurses was high. Nurses with a female sex, nurses who had chronic disease, BSC holders and nurses who had poor sleep quality had a poor level of work ability. The federal Minister of health and the study hospitals collaborative with concerned stakeholders to design strategies to enhance work ability among nurses. [ABSTRACT FROM AUTHOR]

In developing countries, one-quarter of young women have suffered from anemia. However, the available studies in Ethiopia have been usually used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of anemia among youth girls in Ethiopia. A total of 5642 weighted samples of young girls from the 2016 Ethiopian Demographic and Health Survey dataset were utilized. The data underwent preprocessing, with 80% of the observations used for training the model and 20% for testing. Eight machine learning algorithms were employed to build and compare models. The model performance was assessed using evaluation metrics in Python software. Various data balancing techniques were applied, and the Boruta algorithm was used to select the most relevant features. Besides, association rule mining was conducted using the Apriori algorithm in R software. The random forest classifier with an AUC value of 82% outperformed in predicting anemia among all the tested classifiers. Region, poor wealth index, no formal education, unimproved toilet facility, rural residence, not used contraceptive method, religion, age, no media exposure, occupation, and having more than 5 family size were the top attributes to predict anemia. Association rule mining was identified the top seven best rules that most frequently associated with anemia. The random forest classifier is the best for predicting anemia. Therefore, making it potentially valuable as decision-support tools for the relevant stakeholders and giving emphasis for the identified predictors could be an important intervention to halt anemia among youth girls. [ABSTRACT FROM AUTHOR]