The Prevalence of Depression and Anxiety Disorders Among Postgraduate Students in an Arabic Sample: A Cross-sectional Study
Bibliographic record
Abstract
Abstract Background: Depression and anxiety are indicators of mental health and quality of life. Studies found a high prevalence of depression and anxiety disorders among specific populations, such as medical students, residents and fellows. However, postgraduate students unarguably suffer from many private and career life stressors. Unfortunately, research about the prevalence of depression and anxiety among postgraduate students are greatly lacking.Methods: A cross-sectional online survey; a self-questionnaire divided into five sections. Socio-demographic characteristics, the Patient Health Questionnaire for Depression (PHQ9), Generalized Anxiety Disorder 7 item (GAD7), insomnia and suicide. Results: The number of participants was 1,005, The prevalence of depression and GAD that warrant treatments are 27.4% and 23.6%, respectively. Most of the participants who screened positive for depression and GAD were not aware of having these disorders. Females were at a higher risk of depression (OR: 1.5, 95% CL: 1.10 to 2.15) and GAD (OR:1.49, 95% CL 1.07 to 2.07). Insomnia is associated significantly with depression (P<0.001) and GAD (P<0.001). Depression increases the risk for active suicide thoughts (OR= 7.453) (P<0.001). Limitations: Due to the nature of cross-sectional studies, causal relationships cannot be identified.Conclusion: We have identified a higher prevalence of depression and GAD among postgraduate students compared with the general population. However, they appear to be underrepresented in mental health literature, so further research is necessary.
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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 | 0.002 |
| 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 | 0.000 |
| 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".