Feeling detached: The central role of detachment in a network study of posttraumatic stress symptoms in Public Safety Personnel
Bibliographic record
Abstract
Background: Due to the nature of their work, Public Safety Personnel (PSP; e.g., firefighters, paramedics, police officers) are frequently exposed to potentially psychological traumatic events (PPTE) and are at increased risk of developing posttraumatic stress symptoms (PTSS) compared to the general population. To date, there are a limited number of published studies that have used the statistical tools of network analysis to examine PTSS in PSP, typically relying on small, homogenous samples. Basic procedures: The current study used a large (n = 5,319) and diverse sample of PSP to estimate a network of PTSS and exploratory graph analysis to assess alternative structures of symptom clustering, compared to tradi- tional latent models. Main findings: The results of the analyses estimated two symptom clusters which differed from most latent models of PTSS. Re-experiencing and avoidance symptoms clustered together, instead of in two clusters. Similarly, hy- perarousal symptoms (hypervigilance, sleep disturbance, startle reflex, concentration difficulties) clustered in a single community instead of two or three clusters in many latent models of PTSS. The symptom of detachment played the most central role in the network and acted as a bridge symptom between numerous clusters of symptoms. The least central symptom was amnesia, which also had the most inconsistent pattern of clustering and bridging. Other bridge symptoms included negative emotions, difficulty concentrating, and reckless behaviour. Principal conclusions: The symptom of detachment played a pervasive role in centrality and bridging in a network of PTSS in PSP. Future research is necessary to identify whether central PTSS differ across populations based on their PPTE type (e.g., combat, assault, rape) or typical environmental factors (e.g., group cohesion in PSP and military).
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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 | 0.003 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".