Prevalence and Predictors of Psychological Distress Among University Students in Herat, Afghanistan
Bibliographic record
Abstract
Objectives: University students are at an elevated risk for psychological distress, yet there is limited research on this issue in conflict-affected settings like Afghanistan. This study aimed to estimate the prevalence of psychological distress and identify key demographic and academic predictors among university students in Herat, Afghanistan. Methods: A cross-sectional study was conducted from September to October 2024 at Jami University. A total of 346 students were selected through stratified random sampling. A self-administered, paper-based questionnaire collected sociodemographic data and assessed psychological distress using the 12-item General Health Questionnaire (GHQ-12), with a score ≥3 indicating distress. Data were analyzed using Chi-square tests and multivariate binary logistic regression. Results: The overall prevalence of psychological distress among students was 46.8%. The multivariate regression analysis identified several significant predictors. Students in their 7th academic year were more likely to experience distress compared to 1st-year students (OR = 2.081). Living in a dormitory was associated with lower odds of distress compared to living at home (OR = 0.417). Students in the faculties of Sharia (OR = 0.343) and Medicine (OR = 0.384) had lower odds of distress than Economics students. Furthermore, students whose mothers held a bachelor's degree had significantly higher odds of distress (OR = 2.786) compared to those with illiterate mothers. Conclusion: Many university students in Herat experience psychological distress, linked to factors like academic year, living situation, field of study, and maternal education. Targeted campus mental health services are urgently needed.
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".