Traumatic Events, Psychopathology, and Post-Traumatic Stress Disorder in the General Community and First Responders: Presence of Symptoms and Associated Factors
Bibliographic record
Abstract
Most individuals experience at least one traumatic event during their lifetime, which can lead to the development of psychopathological symptoms and Post-Traumatic Stress Disorder (PTSD). First responders (e.g., police officers, firefighters, emergency medical professionals) are exposed to traumatic events daily, making them particularly vulnerable to developing such symptoms. Using an online questionnaire, this study aimed to compare self-reported exposure to traumatic events and the presence of psychopathological and PTSD symptoms between a sample from the general community (n = 137) and first responders (n = 672) residing in Portugal. We also sought to identify factors associated with the development of PTSD symptoms. Results showed that although first responders reported higher exposure to traumatic events, there were no significant differences in PTSD symptoms between first responders and the general community. However, general psychopathological symptoms, particularly anxiety and depression, were higher in the general community than among first responders. Symptoms of anxiety, depression, obsessive–compulsive tendencies, hostility, paranoid ideation, psychoticism, and personally experienced traumatic events emerged as significant predictors of PTSD symptoms, whereas demographic variables showed no significant predictive value. The potential influence of factors such as terror management theory, training and education, professional selection, the “hero lifestyle”, and the “police culture” is discussed, along with implications and directions for future research.
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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 | 0.001 |
| 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) | 0.003 | 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".