Trauma Exposure and Health: The Role of Depressive and Hyperarousal Symptoms
Bibliographic record
Abstract
Posttraumatic stress disorder (PTSD) and depressive symptoms have been theorized to mediate the relationship between trauma exposure and physical health symptoms. Although empirical evidence supports this premise, studies conducted to date have employed statistical mediation analyses that are now broadly criticized. Furthermore, the mediating roles of both PTSD and depressive symptoms have seldom been examined concurrently, and it remains unclear which PTSD symptom clusters uniquely mediate this relationship. The aim of the present study was to examine the mediating role of reexperiencing, avoidance/numbing, hyperarousal, and depressive symptoms in the relationship between trauma exposure and physical health symptoms. Participants were 516 Spanish female undergraduate students. Physical health symptoms were compared between those who reported trauma exposure (n = 266) and those who did not (n = 250). Data from trauma-exposed participants were analyzed using regression models with bootstrapping to test mediation. Results of the analyses showed that the trauma-exposed group reported significantly more physical health symptoms (r(2) = .035). Hyperarousal and depressive symptoms uniquely mediated the relationship between trauma exposure and physical health symptoms. Our findings clarify some of the mechanisms by which negative health consequences occur subsequent to trauma exposure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".