Trauma echoes: factors associated with peritraumatic distress and anxiety five days following Iranian missile attack on Israel
Bibliographic record
Abstract
Introduction: On 13–14 April 2024, Iran launched ∼300 drones and missiles at Israel, in an unprecedented attack. As most studies examine the effects of trauma months or years later, less is known about its effects days later. To fill this gap, this study gauged the population response, five days after the attack. Specifically, we examined the prevalence and factors associated with two precursors for later development of PTSD, peritraumatic distress (PD) and generalized anxiety disorder (GAD).Methods: Five-hundred and fifty-three participants (Mage = 57.51, SD = 13.67 years, range [30–90], 48.3% females) reported their distal and proximal exposure to traumatic events, probable PTSD due to Israel-Hamas-War, sleeping troubles, and media information consumption during the event.Results: Logistic regressions indicated that, after adjusting for demographics, clinical levels of PD and GAD (respectively, using the accepted cutoffs) were significantly linked to probable PTSD due to the Israel-Hamas War (PD:OR = 4.066, 95%CI: 2.236–7.393, p < .001; GAD:OR = 2.397, 95%CI: 1.285–4.471, p = .006), sleeping troubles (PD:OR = 1.248, 95%CI: 1.186–1.314, p < .001; GAD:OR = 1.325, 95%CI: 1.242–1.413, p < .001) and media consumption (PD:OR = 1.442, 95%CI:1.17–1.777, p = .001; GAD:OR = 1.515, 95%CI: 1.144–2.007, p = .004), but not to previous trauma (life-long exposure or Israel-Hamas war).Discussion: Results suggest that previous psychopathology, stress-related reactions (sleeping) and actions (media consumption), rather than previous exposures to traumatic events are the primary indices related to PD and GAD in the first days after exposure to war-related traumatic events. Findings highlight the importance of early detection of reactions and symptoms following trauma exposure. The main limitation of the study is its cross-sectional design. Future longitudinal studies are needed to understand the developmental trajectory of these effects.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".