Depression symptoms and mental health well-being among Bangladeshi nursing students
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".