Dissociative symptoms predict severe illness presentation in Canadian public safety personnel with presumptive post-traumatic stress disorder (PTSD)
Bibliographic record
Abstract
Background: Post-traumatic stress disorder affects 9% of individuals across their lifetime and increases nearly fourfold to 35% in Canadian public safety personnel (PSP). On-the-job experiences of PSP frequently meet criteria for traumatic events, making these individuals highly vulnerable to exposures of trauma and the negative consequences of PTSD. Few studies have reported on the clinical characteristics of Canadian samples of PSP and even fewer have examined the dissociative subtype of PTSD, which is associated with more severe, chronic traumatic experiences, and worse outcomes. Objective: This study aimed to characterize dissociative symptoms, PTSD symptom severity, and other clinical variables among Canadian PSP with presumptive PTSD. Methods: = 50) that were enrolled in a psychological intervention. Only baseline testing data (prior to any intervention) were analysed in this study, such as PTSD symptom severity, dissociative symptoms, emotion dysregulation, and functional impairment. Results: In our sample, 24.4% self-reported elevated levels of dissociation, specifically symptoms of depersonalization and derealization. Depersonalization and derealization symptoms were associated with more severe PTSD symptoms, greater emotion dysregulation, and functional impairment. Conclusions: Nearly a quarter of this sample of Canadian PSP reported experiencing elevated levels of PTSD-related dissociation (depersonalization and derealization). These high levels of depersonalization and derealization were consistently positively associated with greater illness severity across clinical measures. It is imperative that dissociative symptoms be better recognized in patient populations that are exposed to chronic traumatic events such as PSP, so that treatment interventions can be designed to target a more severe illness presentation.
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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 | 0.002 | 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) | 0.004 | 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".