Exploring Dissociation in Post-Traumatic Stress Disorder: Impact on Emotion, Cognition, and Daily Functioning Among Military Members, Veterans, and First Responders in Canada
Bibliographic record
Abstract
Military members, veterans, and public safety personnel in Canada experience more frequent and severe symptoms of post-traumatic stress disorder (PTSD) as compared to the general population. Up to a third of individuals with PTSD experience persistent trauma-related dissociation symptoms, including depersonalization (feeling detached from oneself) and derealization (feeling detached from the world). Dissociative presentations reflect hypoarousal (e.g., emotional numbing and blunted affect) and contrast with classic PTSD symptoms of hyperarousal (e.g., hypervigilance and physiological reactivity). Although dissociation is linked to severe trauma and PTSD, research focusing on classic PTSD symptoms has dominated the trauma literature. To address this gap in research, I explored the impact of dissociation on emotion regulation, cognition, and daily functioning among adults seeking treatment for PTSD in Canada. In Chapter 2, I characterized dissociation symptoms in a sample public safety personnel. Approximately 25% of individuals reported elevated dissociation, which was associated with greater PTSD severity, emotion dysregulation, and daily impairment. In Chapter 3, I examined whether dissociation and emotion dysregulation predict cognitive dysfunction in a sample of military members, veterans, and public safety personnel. Both dissociation and emotion dysregulation symptoms explained, in part, impairments in cognitive functioning, even after accounting for the effects of PTSD severity. In Chapter 4, I trained machine learning models to predict PTSD-related illness in a sample of military members, veterans, public safety personnel, and civilians. Machine learning models accurately predicted self-reported PTSD severity (43% of variance) and functional impairment (32% of variance) in unseen data from patients in a hold-out test set. Both dissociation and emotion dysregulation symptoms emerged as important contributors to predictions. Overall, my findings suggest that improved recognition of trauma-related dissociative symptoms and tailored integration of evidence-based therapies may help address the complex needs of individuals experiencing PTSD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".