Social determinants of student mental health before and after the Beirut port explosion: two cross-sectional studies
Bibliographic record
Abstract
BACKGROUND: Students in Lebanon are facing the devastating impact of multiple national crises, including an unprecedented economic collapse and the Beirut port explosion that killed hundreds, injured thousands, and displaced hundreds of thousands of people. The aim of this study was to identify key social determinants of common mental health symptoms before and after the Beirut port explosion for students at the American University of Beirut, a university based around 4 km from the port. METHODS: Two cross-sectional studies were conducted using a representative sample of undergraduate and graduate students at the American University of Beirut. The study was conducted just before (Study 1) and repeated after the Beirut port explosion (Study 2). RESULTS: A total of 217 students participated (n = 143 in Study 1 and n = 74 in Study 2). In Study 1 before the explosion, poorer family functioning and social support were correlated with higher levels of depressive symptoms, but not with anxiety or trauma symptoms. Financial stress was correlated with depressive and trauma symptoms. In the partially adjusted regression model (adjusting for demographics), only financial stress was significantly associated with depressive symptoms. In the fully adjusted model (adjusting for adversity), financial stress was associated with depressive and anxiety symptoms. In Study 2 after the explosion, poorer family functioning and poorer social support were correlated with higher levels of depressive symptoms, while only poorer social support was correlated with higher levels of anxiety symptoms-trauma symptoms were not correlated with either. Financial stress was correlated with all symptoms. In the partially adjusted regression model, only financial stress was significantly associated with all symptom clusters. In the fully adjusted model, no variables were significant. CONCLUSION: Findings indicate a detrimental impact of financial stress on the mental health of students in Lebanon, beyond the otherwise protective effects of family and social support, in the context of an unprecedented economic crisis and extremely high levels of distress after the explosion. Findings indicate that mental health interventions for college students in Lebanon should include addressing financial stress, and that further research is needed to identify protective factors during acute emergencies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".