Generalized anxiety disorder among Bangladeshi university students during COVID-19 pandemic: gender specific findings from a cross-sectional study
Bibliographic record
Abstract
In the current COVID-19 pandemic there are reports of deteriorating psychological conditions among university students in lower-middle-income countries (LMICs), but very little is known about the gender differences in the mental health conditions on this population. This study aims to assess generalized anxiety disorder (GAD) among university students using a gender lens during the COVID-19 pandemic. A cross-sectional study was conducted using web-based Google forms between May 2020 and August 2020 among 605 current students of two universities in Bangladesh. Within the total 605 study participants, 59.5% (360) were female. The prevalence of mild to severe anxiety disorder was 61.8% among females and 38.2% among males. In the multivariable logistic regression analysis, females were 2.21 times more likely to have anxiety compared to males [AOR: 2.21; CI 95% (1.28-53.70); p-value: 0.004] and participants' age was negatively associated with increased levels of anxiety (AOR = 0.17; 95% CI = 0.05-0.57; p = 0.001). In addition, participants who were worried about academic delays were more anxious than those who were not worried about it (AOR: 2.82; 95% CI 1.50-5.31, p = 0.001). These findings of this study will add value to the existing limited evidence and strongly advocate in designing gender-specific, low-intensity interventions to ensure comprehensive mental health services for the young adult population of Bangladesh.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".