Relationship between specific posttraumatic stress symptoms and suicidality in a sample of American veterans: A network analysis
Bibliographic record
Abstract
Military veterans are at heightened risk for posttraumatic stress disorder (PTSD) and suicidality, yet the relationship between specific posttraumatic stress symptoms (PTSS) and suicidality remains understudied. This study used network analysis to explore the interconnections between PTSS and suicidality in a treatment-seeking veteran sample. The PTSD Checklist for DSM-5 (PCL-5) and the Suicidal Behaviors Questionnaire-Revised (SBQR) were used to assess 19 nodes, including 18 PTSS and one suicidality, in both partial-correlation and Bayesian networks. Key findings revealed that negative emotions, intrusive memories, loss of interest in activities, and physiological reactivity were the most central symptoms in the partial-correlation network. Bayesian analysis further identified negative emotions as the primary causal driver of pathways leading to sleep disturbances, amnesia, and suicidality. Three symptom clusters emerged: intrusion-avoidance-sleep, negative affect-externalization-suicidality, and anhedonia. Results emphasize the importance of targeting negative emotions and related symptoms to disrupt cascading effects and reduce suicidality in veterans. These findings underscore the value of network analysis in identifying clinically actionable insights, enabling more precise and transdiagnostic approaches to PTSD treatment. Future research should incorporate longitudinal designs and additional variables, such as trauma type and resilience, to refine interventions for this high-risk population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".