Anxiety, Depression and Posttraumatic Stress Disorder after Terrorist Attacks: A General Review of the Literature
Bibliographic record
Abstract
Terrorism, though not well-defined, is a violent act that has been shown to have longstanding effects on the mental health of those who witness it. The aim of this general literature review is to explore the effect that terrorism has on posttraumatic stress disorder (PTSD), major depressive disorder (MDD) and anxiety disorders, as well as the bio-psycho-social determinants that mediate its impact. This paper describes the prevalence, risk factors, protective factors, common presentations and interventions identified for PTSD, depression and anxiety disorders occurring following terrorist attacks. We conducted a literature search in MEDLINE using a number of keywords detailed below. After applying inclusion and exclusion criteria, we kept 80 articles, which we summarized in tabular form. A majority of articles found detailed the impact of terrorism on PTSD, and took place in a Western, mainly American setting. The main factors that impacted the presentation of mental illness include gender, ethnicity, social supports, socioeconomic status, level of preparedness, level of exposure, pre-existing trauma and mental illness, and subsequent life stressors. The main intervention detailed in this article as showing evidence post-terrorism is trauma-focused cognitive-behavioural therapy. This study highlights the importance of this topic, and in particular, its implications for public health policy and practice.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".