Depression, anxiety, stress, sleep quality, and life satisfaction among undergraduate medical students in the Gaza Strip: a cross-sectional survey
Bibliographic record
Abstract
Abstract Background Medical students suffer above-average rates of depression, anxiety, stress, poor sleep, and life dissatisfaction, which impacts their performance. In addition to the demands of medical study, medical students in the Gaza Strip face coping with life in an area marred by chronic conflict and poverty. Methods This cross-sectional study assessed medical students at the two medical schools in the Gaza Strip. Students were randomized according to university, sex, and academic level. Screening employed the DASS21, PSQI, and SWLS instruments. Ordinal logistic regression was used to investigate predictors of anxiety, stress, depression, sleep quality, and SWLS stage. Potential predictors of poor sleep quality were assessed by binary logistic regression, and multivariable logistic regression was implemented to determine the effect of covariates. Results Three hundred sixty-two medical students participated. Different stages of depression, anxiety, and stress symptoms were prevalent in 69%, 77.3%, and 65.2% of students, respectively. Poor sleep quality was prevalent in 77.9% of students, and 46.1% of medical students were dissatisfied with their lives. Low income was associated with higher rates of extremely severe anxiety and with higher rates of moderate depression. Poor sleep quality was associated with higher rates of all-stage anxiety, stress, and depression in univariate regression and with higher odds of moderate anxiety and mild, moderate, and extremely severe depression in multivariable regression. Conclusions Medical students in the Gaza Strip suffer from high rates of depression and anxiety symptoms, stress, poor sleep, and life dissatisfaction compared to several other countries and the pooled global prevalence. Medical schools in Gaza should put forward strategies to limit the psychological burdens perceived by their students.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".