The psychiatric impact of the 2020 beirut port explosion on civilians and relief workers
Bibliographic record
Abstract
Introduction On August 4th 2020, a massive port explosion shook Beirut, killing at least 200, injuring more than 6,000 people and leaving more than a quarter of a million living in unfit homes. Various factors can participate in the severity of mental health outcomes of a disaster including the number of injuries, the degree of property destruction, unexpectedness of the occurrence of the event, and the type of the disaster. Objectives The main aim of this study is to assess the prevalence of post-traumatic stress disorder (acute stress disorder) and major depression at 1 and 6 months following the Beirut explosion. The secondary aim is to determine predictors of PTSD incidence among civilians and relief workers affected by the disaster. Methods This is a cross-sectional study with data collected via an online survey through convenience sampling. People will be recruited via social media platforms. To achieve a power of 80% and a two-sided significance of 5% and because gender differences will be explored, assuming a design effect (deff) of 2.5, a minimum sample of 960 participants would be needed. The survey will include sociodemographic data, questions about exposure levels to trauma and a psychiatric symptom inventory. Pearson’s Chi Square test will be used to examine the association between categorical variables and regression models will be run to examine the associations while controlling for confounders, including age, gender and others. Results The results from both rounds of data collection (months 1 and 6) will be available in late March 2021. Conclusions to follow based on results Disclosure No significant relationships.
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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".