Depression symptoms and mental health well-being among Bangladeshi nursing students

Depression and poor mental health well-being are common concerns among nursing students as these outcomes impact students’ academic performance, clinical competence, and quality of life. This study aimed to investigate depression symptoms and mental health well-being, identify their associated predictors, and examine the correlation between depression symptoms and mental health well-being among nursing students in Bangladesh. Between August and October 2022, we collected data for this cross-sectional study via an online questionnaire, which included the PHQ-9 to assess depression symptoms and the WEMWBS-14 to measure mental health well-being. Multiple linear regression models identified predictors of depression and mental health well-being. Pearson correlation assessed the correlation between the depression symptoms score and mental health well-being score. A total of 2174 nursing students participated in the study, with a mean age of 20.96 years. Over 60% of students reported moderate to extremely severe depression symptoms. Age, sex, family pressure to choose nursing, and postgraduate qualifications of teachers were significant predictors of depression symptoms. For mental health well-being, age, sex, mother’s education, type of institution, family pressure to choose nursing, availability of subject-specific teachers, and postgraduate qualifications of teachers were the significant predictors. We observed a statistically significant moderate negative correlation (r = − 0.39) between mental health well-being and depression symptoms. Depression symptoms were highly prevalent among nursing students who participated in this study and were significantly correlated with poor mental health well-being. Demographic and academic predictors, including family pressure to choose nursing and qualifications of teachers, were significant predictors of depression symptoms and mental health outcomes. The findings emphasize the need for mental health interventions and supportive academic environments within nursing education in the context of Bangladesh.

Authors

Institutions

Publication Details

Journal
Discover Social Science and Health
Published
2025-12-01
DOI
https://doi.org/10.1007/s44155-025-00335-w
Citations
1
Primary Topic
Healthcare professionals’ stress and burnout
Type
article
Field-Weighted Citation Impact
2.82
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Depression symptoms and mental health well-being among Bangladeshi nursing students

Sopon Akter, Saifur Rahman Chowdhury, Shimpi Akter, Anjan Kumar Roy et al.
1 citations
Discover Social Science and Health
Healthcare professionals’ stress and burnout
2.82
article

Depression symptoms and mental health well-being among Bangladeshi nursing students

Sopon Akter, Saifur Rahman Chowdhury, Shimpi Akter, Anjan Kumar Roy, Ahmed Hossain, Md. Hasan Al Banna, Masuda Akter, Md Ikbal Hossain, Humayun Kabir, Samiul Amin Chowdhury, Md. Abdul Qaum Mezan
article en
1 citations

Abstract

Depression and poor mental health well-being are common concerns among nursing students as these outcomes impact students’ academic performance, clinical competence, and quality of life. This study aimed to investigate depression symptoms and mental health well-being, identify their associated predictors, and examine the correlation between depression symptoms and mental health well-being among nursing students in Bangladesh. Between August and October 2022, we collected data for this cross-sectional study via an online questionnaire, which included the PHQ-9 to assess depression symptoms and the WEMWBS-14 to measure mental health well-being. Multiple linear regression models identified predictors of depression and mental health well-being. Pearson correlation assessed the correlation between the depression symptoms score and mental health well-being score. A total of 2174 nursing students participated in the study, with a mean age of 20.96 years. Over 60% of students reported moderate to extremely severe depression symptoms. Age, sex, family pressure to choose nursing, and postgraduate qualifications of teachers were significant predictors of depression symptoms. For mental health well-being, age, sex, mother’s education, type of institution, family pressure to choose nursing, availability of subject-specific teachers, and postgraduate qualifications of teachers were the significant predictors. We observed a statistically significant moderate negative correlation (r = − 0.39) between mental health well-being and depression symptoms. Depression symptoms were highly prevalent among nursing students who participated in this study and were significantly correlated with poor mental health well-being. Demographic and academic predictors, including family pressure to choose nursing and qualifications of teachers, were significant predictors of depression symptoms and mental health outcomes. The findings emphasize the need for mental health interventions and supportive academic environments within nursing education in the context of Bangladesh.

Discover Social Science and HealthVol. 6(1)
American International University-Bangladesh (BD), Shahjalal University of Science and Technology (BD), Sylhet Agricultural University (BD), University of Alberta (CA), North South University (BD), Leading University (BD), University of Dhaka (BD), University of Sharjah (AE), Patuakhali Science and Technology University (BD), University of Saskatchewan (CA), Impact (CA), Bangladesh University of Professionals (BD), McMaster University (CA)
Openalex Percentile: Top 7%
Healthcare professionals’ stress and burnout
2.82
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.