Psychological impacts of the Gaza war on Palestinian young adults: a cross-sectional study of depression, anxiety, stress, and PTSD symptoms
Bibliographic record
Abstract
BACKGROUND: The Gaza Strip has been embroiled in a violent military assault since October 2023, with an immense toll on the civilian population. Armed conflicts threaten the mental health of affected communities and survivors, and psychiatric morbidity increases with forced displacement and with severe and recurrent trauma. This study investigates the prevalence and predisposing factors of depression, anxiety, stress, and PTSD symptoms in a group of young adult students from the Gaza Strip during the war. METHODS: A cross-sectional, internet-based survey recruited medical students from the Gaza Strip and used the DASS21, SWLS, and PCL-5 instruments. PTSD diagnosis required having a PCL-5 score ≥ 23 and fulfilling the DSM-5 criteria. Rates of depression, anxiety, stress, and life satisfaction were compared with a previous dataset collected in 2022. Finally, logistic regression models were fitted using R software to identify factors significantly associated with depression, anxiety, stress, and PTSD. RESULTS: Three hundred thirty-nine medical students participated. Most had been displaced several times, and the great majority had lost a relative, colleague, or friend. Also, a majority had lost their homes and income. 97.05% of participants suffered mild depressive symptoms or higher, while 84.37% and 90.56% reported mild anxiety and mild stress symptoms or higher, respectively. High levels of life dissatisfaction were also found, and 63.40% suffered from PTSD. Symptoms were significantly more prevalent than baseline rates. All participants with PTSD had at least one psychiatric comorbidity. Living in a shelter and having moderate stress symptoms or higher were significantly associated with depression. Being a female, losing a friend, having moderate stress symptoms or higher, and having PTSD predicted having moderate anxiety or higher. Having moderate or higher depression symptoms, moderate or higher anxiety symptoms, and PTSD predicted having moderate stress symptoms or higher. Finally, moderate or higher anxiety and stress symptoms predicted having PTSD. CONCLUSION: The study detected very high rates of psychiatric disorders among its population of young adult medical students and outlined a myriad of risk factors associated with higher comorbidity. Interventions are needed to prevent a brewing mental health crisis in the Gaza Strip.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".