Factors influencing mental health outcomes among university students: a cross-sectional study in Bangladesh
Bibliographic record
Abstract
INTRODUCTION: Mental health issues, particularly anxiety and depression, are on the rise among university students globally, including in Bangladesh. However, comprehensive data on the factors influencing mental health outcomes in this group remain limited, hindering the development of effective programmes and interventions. OBJECTIVES: This study aims to assess the mental health status of university students in Bangladesh and examine the key factors influencing mental health outcomes. DESIGN: A cross-sectional online survey was conducted in Bangladesh from December 2022 to March 2023. SETTING: Universities in Bangladesh. PARTICIPANTS: University students aged 18 and older. OUTCOME MEASURES: Data were collected through a structured survey that assessed depression and anxiety using the Patient Health Questionnaire and the Generalized Anxiety Disorder scale, as well as dietary diversity through the Individual Dietary Diversity Score. RESULTS: The results showed that while female students exhibited greater dietary diversity, they also had higher obesity rates, whereas male students reported more physical activity. Mental health assessments revealed that 36.1% of participants experienced mild anxiety, 11.5% severe anxiety, 39.8% mild depression and 8.3% severe depression. Binary logistic regression analysis identified significant predictors of anxiety and depression, including gender, personal income, body mass index and screen time. Females were less likely to experience anxiety (crude odds ratios (COR): 0.531, p =0.034) and depression (COR: 0.591, p =0.023) compared with males. Furthermore, low intake of wheat, rice (COR: 2.123, p=0.050) and pulses (COR: 1.519, p=0.050), as well as high consumption of fats, oils (COR: 2.231, p=0.024) and sugary foods (COR: 2.277, p=0.001), were associated with anxiety, while inadequate intake of vitamin A- and C-rich fruits (COR: 1.435, p =0.018) was linked to depression. Overweight students were found to be more susceptible to depression. CONCLUSION: The findings of the study emphasise the necessity for targeted interventions that promote healthier lifestyles to enhance mental health outcomes among university students in Bangladesh.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".