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The unprecedented COVID-19 pandemic has become a global burden disrupting peoples' quality of life. Students being an important cohort of a country, their mental health during this pandemic has been recognized as a concerning issue. Therefore, the prevalence and associated risk factors of Bangladeshi students' mental health sufferings (ie, depression, anxiety, and stress) are systematically reviewed herein for the first time. Adhering to the PRISMA guideline, a systematic search was performed from 1 to 5 April, 2021 in several databases including PubMed; and finally, a total of 7 articles were included to this review. The prevalence rates of mild to severe symptoms of depression, anxiety, and stress ranged from 46.92% to 82.4%, 26.6% to 96.82%, and 28.5% to 70.1%, respectively. The risk factors concerning mental health problems included the factors related to (i) socio-demographic (younger age, gender, lower educational grade, urban residence, family size, currently living with family/parents, and having children in the family), (ii) behavior and health (smoking status, lack of physical exercise, more internet browsing time, and dissatisfaction with sleep), (iii) COVID-19 pandemic- (COVID-19 related symptoms, COVID-19 related perceptions, and fear of COVID-19 infection), (iv) miscellaneous (losing part-time teaching job, lack of study concentration, agitation, fear of getting assaulted or humiliated on the way to the hospital or home, financial problems, academic dissatisfaction, inadequate food supply, higher exposure to COVID-19 social and mass media, engaging with more recreational activities, and performing more household chores). The overall assumption of mental disorders' prevalence rates can be regarded as problematic to this cohort. Thus, the authorities should consider setting up possible strategies to diminish the pandemic effect on students' mental health.

Substance abuse is a major concern worldwide and is increasing rapidly in Bangladesh. However, there are no prior studies concerning lifestyle-related factors that influence adolescents’ substance use behavior. Therefore, the present study investigated the prevalence of substance use and its associated sociodemographic and lifestyle-related risk factors among a total of 424 Bangladeshi high school-going adolescents through a structured questionnaire interview study. The survey questionnaire consisted of socio-demographics, lifestyle-related information, and substance use-related questions. For data analysis, descriptive and inferential statistics were performed using SPSS (Statistical Package for Social Science) version 22.0, and a p -value of <0.05 determined statistical significance. Results showed that 21.2%, 14.4%, and 15.1% of the participants reported smoking, using a drug, and consuming alcohol, respectively, at least once during their lifespan; whereas the current (i.e., past-month) rates were reported to be 10.4%, 2.8%, and 3.1%, respectively. Overall, the current substance use risk factors were identified as being male, not being from science academic background, having less family influence on personal life, irregular teeth brushing, being smartphone users, using a smartphone for a longer time, and being late-night sleepers. From the list of identified risk factors of substance use, those that are modifiable may be targeted to evolve a prevention program to manage this problem in Bangladeshi adolescents.

Problematic smartphone use (PSU) has been increasing hastily in recent decades, and it has become inseparable during the COVID-19 pandemic, especially among the students who are at risk of problematic smartphone use. Therefore, the present study aimed to investigate the prevalence and associated factors of PSU during the COVID-19 pandemic among the Bangladeshi students. A total of 601 Bangladeshi students were recruited through an online-based cross-sectional survey that was conducted between October 7 and November 2, 2020. The survey collected information related to socio-demographics, behavioral health, internet use behaviors, depression, anxiety, and PSU. Independent samples -test and one-way ANOVA were performed to present the relationship between the studied variables and PSU. Multiple linear regression analysis was also used for investigating the explanatory power of the predictive models for PSU. Surprisingly, about 86.9% of the students scored to be problematic smartphone users (≥21 out of a total 36 based on the Smartphone Application-Based Addiction Scale). In addition, medical students, engaging in a relationship, performing less physical activity, longer duration of internet use, some sorts of internet use purpose (eg, messaging, watching videos, using social media), depression, and anxiety were significantly associated with higher scores of PSU. After adjusting all the studied variables, the final model explained a 31.3% variance predicting PSU. The present study is one of the first approaches to assess the prevalence of PSU among the Bangladeshi students during the COVID-19 pandemic, whereas the addiction level was superfluous (and this may be due to more online engagement related to the pandemic). Thus, the study recommended strategies or policies related to the students' risk-reducing and healthy use of smartphones.

Background Prospective university students are regarded as highly vulnerable to psychological issues, including suicide. Despite the complexity of suicidal behaviors, innovative methodologies like Geographic Information System (GIS) mapping and Machine Learning have not been fully explored for predictive modeling and risk assessment. This study aims to investigate the prevalence and risk factors associated with suicidal behavior, offering a thorough understanding of the spatial distribution and predictive factors of suicidality. Methods Data from 1,485 participants were collected on socio-demographic characteristics, admission-related variables, health behaviors, and familial factors. Logistic regression analysis identified significant risk factors, while Machine Learning algorithms, including CatBoost and K-Nearest Neighbors, were used for prediction. Results The findings revealed a 20.5% prevalence of suicidal thoughts, with disparities across demographics and behaviors. Female participants, rural dwellers, and those from joint families exhibited higher suicidality rates. Repeat test-takers, academically struggling students, and those not coached professionally displayed elevated risks. Moreover, substance use, mental health issues, and family mental health and suicide history increased odds of suicidal behavior. GIS mapping identified regional variations, notably in the Sylhet division and Chittagong Hill Tracts. While, Machine Learning models were used to predict suicidal thoughts, with depression status as the most influential factor. Among all models, CatBoost achieved the best overall performance, with the lowest log loss, highest AUC, and strongest 95% confidence interval. KNN also performed well in accuracy, precision, and F1-score, but showed a slightly higher log loss, making CatBoost the most reliable model for predicting suicidal thoughts. Conclusions This study emphasizes the multifaceted nature of suicidal behavior, emphasizing the need for targeted interventions and support services to address mental health challenges and prevent suicide in this vulnerable population.

Background Insomnia is highly prevalent among individuals with chronic diseases and often exacerbates disease progression while adversely impacting mental health and quality of life. However, validated tools to assess insomnia in this vulnerable population remain limited in emerging economies. This study aimed to evaluate the psychometric properties of the Bangla version of the Insomnia Severity Index (ISI) among Bangladeshi adults with chronic diseases using confirmatory factor analysis (CFA), Rasch modeling, and network analysis. Methods A face‐to‐face survey was conducted among adults with clinically diagnosed chronic illnesses. CFA was used to test one‐, two‐, and three‐factor structures and gender‐based measurement invariance. Rasch analysis examined item fit, reliability, and response category functioning. Network analysis estimated symptom interrelations, centrality, bridge metrics, and gender‐based network invariance. Results The two‐factor model (Night Symptoms, Daytime Impact) provided the most favorable balance between statistical fit and theoretical coherence. Although the three‐factor model yielded marginally better indices, its reliance on a single‐indicator factor and high inter‐factor correlations limited its interpretability. All items showed strong factor loadings and internal consistency. Measurement invariance was supported across gender. Rasch modeling confirmed item fit, category functioning, and reliability, though some items exhibited moderate gender‐based DIF. Network analysis identified ISI_1 (difficulty falling asleep) and ISI_6 (noticeability of sleep problems) as central symptoms, while ISI_4 (satisfaction with sleep pattern) emerged as a key bridge symptom. Predictability was high, and network structure was invariant across gender. Conclusion The ISI‐Bangla demonstrates strong psychometric validity in a chronic disease population, supporting its use for clinical and research purposes in Bangladesh. Testing the alternative factor structures confirmed the robustness of the two‐factor specification. The integration of CFA, Rasch, and network analysis provides a comprehensive validation framework. The Bangla version of the ISI was validated among adults with chronic diseases using confirmatory factor analysis, Rasch modeling, and network analysis. Findings supported a two‐factor structure (nighttime symptoms, daytime impact) with strong reliability, measurement invariance across gender, and coherent symptom networks. Key central and bridging symptoms highlight clinically actionable targets, establishing the ISI‐Bangla as a robust tool for both clinical and research use in Bangladesh.

Background Phubbing, a phenomenon of ignoring others in face-to-face conversations due to mobile phone use, can be assessed using a Phubbing Scale (PS). Recently, the PS has been shortened into an eight-item version, the PS-8. However, psychometric properties of the PS-8 among Iranian, Bangladeshi and Pakistani individuals remain understudied, especially using advanced psychometric testing, such as Rasch and network analyses. Methods Participants residing in Iran, Bangladesh, and Pakistan (n = 1902; 50.4% females; mean age = 26.3 years) completed the PS-8 and the Internet Disorder Scale-Short Form (IDS9-SF) via an online survey. Network analysis was used to examine if PS-8 items were differentiated from IDS9-SF items; confirmatory factor analysis (CFA) was used to examine the factor structure and measurement invariance of the PS-8; Rasch modeling was used to examine the dimensionality of the PS-8 and differential item functioning (DIF). Results Network analysis showed that PS-8 items were clustered together with a distance to the IDS9-SF items. The CFA results supported a two-factor structure of the PS-8, and the two-factor structure was found to be invariant across countries and women and men. Rasch model results indicated that the two PS-8 subscales were both unidimensional and did not display DIF across countries and gender/sex. Conclusion The PS-8 is a feasible and robust instrument for healthcare providers, especially mental health professionals, to quickly assess and evaluate individuals’ phubbing behaviors.

Bangladeshi flood survivors are reported with such higher mental disorders that are not ever observed in any other cohorts. Although there are a few studies that assessed mental disorders, suicide or suicidal behaviors are not investigated yet. Hence, the present study for the first time investigated suicidal behaviors and its relationship with socio-demographics, flood effects and psychopathology. A cross-sectional interview study was carried out between November and December 2019, after 4/5 months of the flood occurrence. Two completely affected villages from two districts residing in two parts of the country were randomly selected (whereas Manikganj district was less affected by the recent flood compared to Kurigram), and a total of 348 flood survivors were interviewed (45.53 ± 14.85 years). Questions related to basic socio-demographics, flood effects, psychological impacts, and suicidal behaviors were asked in the interviews. In the total sample, 57.5% of flood survivors reported having suicidal ideation, whereas 5.7% and 2.0% madea suicide plan and suicide attempt, respectively. Within two study sites, participants belonging to Kurigram reported significantly higher suicidal ideation compared to Manikganj (84.8% vs 33.2%, = 94.475, <0.001). Belonging to a lower-class family, having less education, and less earning members in the family, being affected severely by the flood, suffering from depression, anxiety, and PTSD, and experiencing financial threat, and economic hardship were suicidal behavior risk factors in the total sample. Considering the present findings (ie, suicidality commensurately increases with flood effects), a multi-sectoral policy and its effective implementation should be adopted for alleviating the flood-related psychological burdens.

Background The surge in digital media consumption, coupled with the ensuing consequences of digital addiction, has witnessed a rapid increase, particularly after the initiation of the COVID-19 pandemic. Despite some studies exploring specific technological addictions, such as internet or social media addiction, in Bangladesh, there is a noticeable gap in research focusing on digital addiction in a broader context. Thus, this study aims to investigate digital addiction among students taking the university entrance test, examining its prevalence, contributing factors, and geographical distribution using GIS techniques. Methods Data from a cross-sectional survey were collected from a total of 2,157 students who were taking the university entrance test at Jahangirnagar University, Bangladesh. A convenience sampling method was applied for data collection using a structured questionnaire. Statistical analyses were performed with SPSS 25 Version and AMOS 23 Version, whereas ArcGIS 10.8 Version was used for the geographical distribution of digital addiction. Results The prevalence of digital addiction was 33.1% (mean score: 16.05 ± 5.58). Those students who are attempting the test for a second time were more likely to be addicted (42.7% vs. 39.1%), but the difference was not statistically significant. Besides, the potential factors predicted for digital addiction were student status, satisfaction with previous mock tests, average monthly expenditure during the admission test preparation, and depression. No significant difference was found between digital addiction and districts. However, digital addiction was higher in the districts of Manikganj, Rajbari, Shariatpur, and Chittagong Hill Tract areas, including Rangamati, and Bandarban. Conclusions The study emphasizes the pressing need for collaborative efforts involving educational policymakers, institutions, and parents to address the growing digital addiction among university-bound students. The recommendations focus on promoting alternative activities, enhancing digital literacy, and imposing restrictions on digital device use, which are crucial steps toward fostering a healthier digital environment and balanced relationship with technology for students.

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.

Background Social media has become integral to daily life, but problematic social media use (PSMU) is an emerging public health concern. Few studies have specifically examined PSMU among university admission test‐takers. This study aimed to investigate the prevalence and predictors of PSMU, the mediating role of social media use duration and the moderating effect of perceived social support on the relationship between psychological distress and PSMU among university entrance test‐takers in Bangladesh. Method A cross‐sectional study was conducted in February 2025, involving 1139 students preparing for university admission tests. Data on sociodemographic, admission‐related factors, mental health symptoms, perceived stress, social support, and PSMU were collected. Data analysis involved Chi‐square tests, logistic regression, and structural equation modeling (SEM) using IBM SPSS 26 and R (lavaan package). Results The prevalence of PSMU was 21.2%. Logistic regression analysis revealed that social media use duration, cigarette smoking, fracture in body parts, depression (OR = 1.60, 95% CI = 1.10–2.34), and high stress (OR = 1.65, 95% CI = 1.03–2.64) had significantly increased odds of developing PSMU. Participants with moderate social support had higher likelihood of PSMU (OR = 1.51, 95% CI: 1.05–2.16). SEM analysis indicated that anxiety (β = 0.37, p = 0.009) and stress (β = 0.27, p < 0.001) had significant direct effects on PSMU, whereas depression did not directly influence PSMU. Social media use duration significantly mediated 24.7% of the effect of stress on PSMU (indirect β = 0.089, p = 0.003), but no significant mediation was found for anxiety or depression. Perceived social support did not significantly moderate the relationships between psychological distress and PSMU. Conclusion Anxiety, stress, and social media usage duration contribute to PSMU. These results inform targeted interventions to mitigate PSMU behaviors and support mental health in this vulnerable group. Problematic social media use (PSMU) affected 21.2% of Bangladeshi university entrance test‐takers. Smoking, fractures, stress, depression, moderate social support, and longer social media use duration were key risk factors. Social media use duration mediated the effect of stress on PSMU, while perceived social support showed no moderation.