Aim: Interoception, defined as the perception of internal bodily changes. The aim of this study is to evaluate the Turkish validity and reliability study of the Multidimensional Assessment of Interoceptive Awareness) Scale (MAIA 2 developed by William Mehling et al. (2018). Material and Method: The research is methodological. Research data was collected between April 2020 and May 2020 (N= 400). Forward and backward translation were used to translate the MAIA-2 into Turkish. We conducted exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to analyze the psychometrics of the MAIA-2 in a 70:30 split sample. Statistical analysis were done with R Project. Results: The Kaiser-Meyer-Olkin value (KMO=0.910) was acceptable, and there exists a significant correlation structure with Bartlett’s test of sphericity (χ2=5134.120, p 0.60). Standardized loadings were positive and >0.40 with significant results (p

More recently a large amount of interest has been devoted to the use of Bayesian methods for deriving parameter estimates of the stochastic frontier analysis. Bayesian stochastic frontier analysis (BSFA) seems to be a useful method to assess the efficiency in energy sector. However, BSFA results do not expose the multiple relationships between input and output variables and energy efficiency. This study proposes a framework to make inferences about BSFA efficiencies, recognizing the underlying relationships between variables and efficiency, using Bayesian network (BN) approach. BN classifiers are proposed as a method to analyze the results obtained from BSFA.

Aim: This study aimed to examine the physical symptoms, coping strategies for earthquake stress and factors predicting coping strategies among adults staying in tent cities after an earthquake.Design: A cross‐sectional design was conducted.Methods: The study sample consisted of 706 adults residing in tent cities in three cities of earthquake‐affected regions. Data were collected through face‐to‐face surveys and the Earthquake Coping Strategies Scale, starting from the 16th day after the earthquake in February–March 2023. Independent samples t‐test, one‐way analysis of variance, McNemar test and multiple linear regression analysis used for data analysis.Results: The first three physical symptoms with the highest frequency after the earthquake were insomnia, frequent waking and difficulty falling asleep, respectively. Individuals with chronic illnesses, lower education levels, no previous experience with earthquakes and having undamaged homes more frequently used religious coping strategies. Individuals with higher incomes, employed individuals, non‐smokers and those with having undamaged homes more frequently used positive reappraisal strategies.Conclusion: The present study concluded that physical symptoms were common among individuals in the post‐earthquake period and that many individuals adopted religious coping strategies.Implications for the Profession and/or Patient Care: Nurses can develop effective interventions for preventing physical and mental health problems in the post‐earthquake period. They can contribute significantly as caregivers, leaders, researchers and advisors in health monitoring and improvement practices for individuals after earthquakes.Impact: The results of the study are instructive for designing prevention and improvement practices to minimise future destructive effects of earthquakes. Individuals mostly experienced sleep problems and preferred different coping styles in the early post‐earthquake period. The physical and mental health of individuals can be improved by early intervention and encouraging effective coping methods after an earthquake.Reporting Method: STROBE checklist.Patient or Public Contribution: No patient or public involvement.

Background/Objectives: Fetal health is essential in prenatal care, influencing both maternal and fetal outcomes. Cardiotocography (CTG) monitors uterine contractions and fetal heart rate, yet manual interpretation exhibits significant inter-examiner variability. Machine learning offers automated alternatives; however, class imbalance in CTG datasets where pathological cases constitute less than 10% leads to poor detection of minority classes. This study aims to provide the first systematic benchmark comparing five resampling strategies across seven classifier families for multi-class CTG classification, evaluated using imbalance-aware metrics rather than overall accuracy alone. Methods: Seven machine learning models were employed: Naïve Bayes (NB), Random Forest (RF), Linear Discriminant Analysis (LDA), k-Nearest Neighbors (KNN), Linear Support Vector Machine (SVM), Multinomial Logistic Regression (MLR), and Multi-Layer Perceptron (MLP). To address class imbalance, we evaluated the original unbalanced dataset (base) and five resampling methods: SMOTE, BSMOTE, ADASYN, NearMiss, and SCUT. Performance was evaluated on a held-out test set using Balanced Accuracy (BACC), Macro-F1, the Macro-Matthews Correlation Coefficient (Macro-MCC), and Macro-Averaged ROC-AUC. We also report per-class ROC curves. Results: Among all models, RF proved most reliable. Training on the original distribution (base) yielded the highest BACC (0.9118), whereas RF combined with BSMOTE provided the strongest class-balanced performance (Macro-MCC = 0.8533, Macro-F1 = 0.9073) with a near-perfect ROC-AUC (approximately 0.986–0.989). Overall, resampling effects proved model dependent. While some classifiers achieved optimal performance on the natural class distribution, oversampling techniques, particularly SMOTE and BSMOTE, demonstrated significant improvements in minority class discrimination and class-balanced metrics across multiple model families. Notably, certain models benefited substantially from resampling, exhibiting enhanced Macro-F1, BACC, and minority class recall without sacrificing overall accuracy. Conclusions: These findings establish robust, model-agnostic baselines for CTG-based fetal health screening. They highlight that strategic oversampling can translate improved minority class discrimination into clinically meaningful performance gains, supporting deployment in cost-sensitive and threshold-aware clinical settings.