Major Mental Health Problems of Undergraduate Students in a Private University of Dhaka, Bangladesh

Hoque, Roksana

doi:10.1016/s0924-9338(15)31442-5

Cited by 9 publications

(5 citation statements)

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“…University counseling services in Australia, the UK, and the USA are reporting increases in help-seeking, with more students presenting with more severe problems [ 49 , 61 – 63 ]. Although there is no noticeable awareness for university counseling services in Bangladesh, a reasonable number of researches carried out to address the prevalence of mental health problems among university students [ 9 , 24 – 28 , 64 , 65 ], even during the COVID-19 pandemic [ 66 – 68 ].…”

Section: Discussionmentioning

confidence: 99%

Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms

Rois

Ray

Rahman

et al. 2021

J Health Popul Nutr

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Background Stress-related mental health problems are one of the most common causes of the burden in university students worldwide. Many studies have been conducted to predict the prevalence of stress among university students, however most of these analyses were predominantly performed using the basic logistic regression (LR) model. As an alternative, we used the advanced machine learning (ML) approaches for detecting significant risk factors and to predict the prevalence of stress among Bangladeshi university students. Methods This prevalence study surveyed 355 students from twenty-eight different Bangladeshi universities using questions concerning anthropometric measurements, academic, lifestyles, and health-related information, which referred to the perceived stress status of the respondents (yes or no). Boruta algorithm was used in determining the significant prognostic factors of the prevalence of stress. Prediction models were built using decision tree (DT), random forest (RF), support vector machine (SVM), and LR, and their performances were evaluated using parameters of confusion matrix, receiver operating characteristics (ROC) curves, and k-fold cross-validation techniques. Results One-third of university students reported stress within the last 12 months. Students’ pulse rate, systolic and diastolic blood pressures, sleep status, smoking status, and academic background were selected as the important features for predicting the prevalence of stress. Evaluated performance revealed that the highest performance observed from RF (accuracy = 0.8972, precision = 0.9241, sensitivity = 0.9250, specificity = 0.8148, area under the ROC curve (AUC) = 0.8715, k-fold accuracy = 0.8983) and the lowest from LR (accuracy = 0.7476, precision = 0.8354, sensitivity = 0.8250, specificity = 0.5185, AUC = 0.7822, k-fold accuracy = 07713) and SVM with polynomial kernel of degree 2 (accuracy = 0.7570, precision = 0.7975, sensitivity = 0.8630, specificity = 0.5294, AUC = 0.7717, k-fold accuracy = 0.7855). Overall, the RF model performs better and authentically predicted stress compared with other ML techniques, including individual and interaction effects of predictors. Conclusion The machine learning framework can be detected the significant prognostic factors and predicted this psychological problem more accurately, thereby helping the policy-makers, stakeholders, and families to understand and prevent this serious crisis by improving policy-making strategies, mental health promotion, and establishing effective university counseling services.

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Section: Discussionmentioning

confidence: 99%

Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms

Rois

Ray

Rahman

et al. 2021

J Health Popul Nutr

View full text Add to dashboard Cite

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“…There are only episodes of anxiety and 4% do not have any psychiatric episodes. However, Pressurizing, the most commonly recorded, the mission and exam workload, and the responsibility to get a good grade were academic variables ( Hoque, 2015 ). In a study, anxiety, and stress, prevalence estimates of depression rates were respectively, 76.1%, 71.5%, and 70.1% for at least mild symptoms, 62.9%, 63.6%, and 58.6% for at least moderate symptoms, 35.2%, 40.3%, and 37.7% for at least severe symptoms and 19.7%, 27.5% and 16.5% for at least very severe Symptoms (S. Islam et al., 2020 ).…”

Section: Introductionmentioning

confidence: 99%

The mental impact of digital divide due to COVID-19 pandemic induced emergency online learning at undergraduate level: Evidence from undergraduate students from Dhaka City

Saha

Dutta

Sifat

2021

Journal of Affective Disorders

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Background COVID-19 outbreak has drawn out institutions to closure with several challenges for university students of undergraduate level in Dhaka city including an emergency shift from traditional learning to online learning, which associated digital divide, left several arguments in response to technological inefficiency, pedagogic inefficiency of teachers, inappropriate study environment and so on. Previous literature shows that the COVID-19 is imposing a threat to mental health all over the nation since its spread. This study intended to evaluate the emerging reasons for psychological distress among university students of undergraduate level in Dhaka, also assess the execution methods, barriers of online learning, and lastly, the attitudes of students regarding online learning throughout the pandemic. Methods A mixed methodology was used to conduct the research. Primary data has been collected using simple purposive sampling on 180 undergraduate students, 9 interviews were taken including 6 in-depth interviews of different university undergraduates from Dhaka city and also 3 (KIIs) from specialists of pedagogy and medical anthropology, and a high official from Bangladesh Telecommunication Regulatory Commission (BTRC). Results Based on the Kessler K-10 distress scale, the study found that the prevalence of no psychological distress (16.67%) followed by mild (40%), moderate (30.56%), and severe psychological distress (12.78%). Conclusion The results concluded considering several reasons for mild to severe psychological distress. The findings suggest some recommendations to accumulate the process of online learning effectively and also strategies to regulate the preferred mode of learning in future.

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“…Previous studies on the mental health of private university students of Bangladesh revealed that 60% of the students were experiencing anxiety, depression and panic episodes and, grading and workload were found to be significant stressors ( Hoque, 2015 ). Another study conducted for determining the causes of health problems on a public university in Bangladesh found 52.2%, 58.1% and 24.9% of the respondents were experiencing severe levels of depression, anxiety and stress respectively and major risk factors were identified as being from a lower-class family, being a cigarette smoker, and engaging in less physical exercise ( Mamun, Hossain, & Griffiths, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Exploring the triggering factors for mental stress of university students amid COVID-19 in Bangladesh: A perception-based study

Shafiq

Nipa

Sultana³

et al. 2021

Children and Youth Services Review

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Major Mental Health Problems of Undergraduate Students in a Private University of Dhaka, Bangladesh

Cited by 9 publications

References 0 publications

Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms

Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms

The mental impact of digital divide due to COVID-19 pandemic induced emergency online learning at undergraduate level: Evidence from undergraduate students from Dhaka City

Exploring the triggering factors for mental stress of university students amid COVID-19 in Bangladesh: A perception-based study

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