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Background: Diabetes management poses significant challenges worldwide, with individuals often facing increased risks of mental health issues, including suicidal behaviors. While traditional statistical methods have been used to assess risk factors, this study introduces machine learning techniques to predict suicidal behaviors among diabetic patients. Therefore, this study aims to investigate the prevalence and risk factors of suicidal behaviors among individuals with diabetes in Bangladesh using both traditional statistical methods and machine learning models. Methods: A cross‐sectional study was carried out with 390 diabetic patients, assessing demographic characteristics, diabetes‐related variables, mental health indicators, and suicidal behaviors. Statistical analyses including chi‐square tests and logistic regression, and various machine learning models, were employed to examine associations and predict risk factors. Results: This study found that 13.8% of the individuals with diabetes reported lifetime suicidal behavior and 9.0% reported past‐year suicidal behavior. Significant risk factors associated with higher rates of suicidal behaviors included younger age, marital status, lower monthly income, higher educational attainment, being insulin users, not having a family history of diabetes, and suffering from anxiety. Among the machine learning models tested, the CatBoost model outperformed others, obtaining low log loss values of 0.45 and 0.32 for lifetime and past‐year suicidal behaviors, respectively. CatBoost demonstrated excellent accuracy rates of 83.97% and 88.46%, significantly higher than those of traditional logistic regression models. The most significant predictors identified by machine learning were treatment type, followed by occupation, anxiety, age group, and having chronic diseases. Conclusion: The findings suggest the urgent need for comprehensive interventions and support systems for individuals managing diabetes, particularly those at higher risk of suicidal behaviors. Machine learning offers improved predictive power and the ability to identify complex patterns in risk factors. Collaborative efforts between healthcare providers, mental health professionals, and public health initiatives are essential for prioritizing mental well‐being in diabetes management. [ABSTRACT FROM AUTHOR]

Aim: This study aims to investigate the relationship between nurses' self‐efficacy and self‐esteem, and their job satisfaction. Design: A cross‐sectional study was conducted. Methods: Employing a random sampling method that included 234 nurses from three hospitals in Iran enrolled. This study utilized the General Self‐Efficacy Questionnaire, Coppersmith Self‐Esteem Inventory, and Minnesota Job Satisfaction Questionnaire. Descriptive analysis, independent t‐tests, Pearson correlation analyses, and linear regression were employed for data analysis. Results: The mean self‐efficacy score for nurses was 26.73 ± 5.62 (out of 40), while self‐esteem and job satisfaction scored 37.13 ± 6.87 (out of 50) and 68.27 ± 12.65 (out of 100), respectively. Significant correlations were found between self‐efficacy, self‐esteem, and job satisfaction. Moreover, self‐esteem and the age group >40 years were identified as important predictors of nurses' job satisfaction. This study highlights the influential role of self‐esteem in determining nurses' job satisfaction. [ABSTRACT FROM AUTHOR]

Background and Aims: Despite evidence that COVID‐19 vaccination can strengthen mental health, there is limited evidence about this in Bangladesh. Thus, this comparative study assessed the prevalence and factors associated with mental health problems between vaccine receivers and nonreceivers. Methods: Using a snowball sampling technique, a web‐based cross‐sectional study was conducted among a total of 459 participants. The survey questionnaire included sociodemographic information, the Patient Health Questionnaire (PHQ—9), the Generalized Anxiety Disorder (GAD—7), and the Trauma Screening Questionnaire (TSQ—10). Results: The study found that mental health problems were nonsignificantly prevalent in the vaccine nonreceivers than those who received it (i.e., 24.79% vs. 20.60% for depression, 21.20% vs. 16.60% for anxiety, and 15.30% vs. 12.60% for posttraumatic stress disorder). Female gender, chronic condition, smoking status, and alcohol consumption were the risk factors for mental health problems. Conclusion: This study's findings suggest that the COVID‐19 vaccination necessarily improves mental health outcomes. However, the study had limitations in terms of its design and sampling technique, and further research is needed to establish a cause‐effect relationship between vaccination and mental health problems. [ABSTRACT FROM AUTHOR]

Getting admission into a university is highly competitive globally and can be regarded as a mental stressor for some individuals. In Bangladesh, most of the universities allow students to repeat the test, whereas repeat test-taking students are supposed to suffer from psychological issues like burnout because of academic non-achievement in their first attempt. But there is no prior study assessing burnout to the population, which was investigated herein. A cross-sectional study was carried out among a total of 911 Bangladeshi university entrance test-taking students (i.e., 49.5% first-time test-takers). The survey measures included socio-demographics, admission-related issues, and mental health problems (depression, anxiety, and burnout). Of the mental health problems, depression, anxiety, and burnout were assessed by the Patient Health Questionnaire (PHQ-9), Generalized anxiety disorder (GAD-7), and Maslach Burnout Inventory—Student Survey (MBI–SS), respectively. In light of two-dimensional and three-dimensional cutoff schemes of the MBI—SS, about 43.7% and 17.3% of the test-taking students, respectively, were classified to be burnout in the total sample. Additionally, 48.1%, 71.4%, and 49.1% of the students in the total sample reported higher emotional exhaustion, higher cynicism, and lower academic efficacy, respectively. In respect to the student status, two-dimensional burnout (48.0% vs. 39.2%; χ2 = 7.164, p = 0.007), and emotional exhaustion (52.2% vs. 43.9%, χ2 = 6.789, p = 0.034) were significantly higher among the repeat test-taking students. Satisfaction with previous mock tests, depression, and anxiety were the associated factors of burnout in all three samples. Based on the present study, it can be concluded that the university entrance test appearing students are highly prevalent to burnout, and the rate increases with academic non-achievement. As the first study assessing burnout to the population, the findings are anticipated to be helpful for policymaking and further studies both inside and outside Bangladesh. [ABSTRACT FROM AUTHOR]