<i>ICD‐11</i> posttraumatic stress disorder (PTSD) and complex PTSD in a sample of prison staff: A latent profile approach
Bibliographic record
Abstract
Although empirical support for the International Statistical Classification of Diseases and Related Health Problems (11th ed.; ICD-11) distinction between posttraumatic stress disorder (PTSD) and complex PTSD (CPTSD) is growing, research into the ICD-11 CPTSD model in prison staff is lacking. This study used latent profile analysis (LPA) to (a) determine if there are distinct groups of trauma-exposed prison governors (i.e., "wardens" in the United States and Canada) who have symptom profiles consistent with the distinction between PTSD and CPTSD and (b) identify predictors and posttraumatic maladaptive beliefs associated with the latent profiles. Trauma-exposed prison governors (N = 385) completed the International Trauma Questionnaire (ITQ) and a measure of traumatic life events. LPA was used to extract profiles using the six ITQ symptom clusters and revealed four profiles: CPTSD (8.4%), PTSD (14.4%), disturbances in self-organization (DSO; 11.0%), and low symptoms (66.3%). Membership in the CPTSD and DSO profiles was associated with cumulative traumatization, odds ratios (OR) = 1.42 and OR = 1.26, respectively, and poorer health, OR = 2.84 and OR = 1.64, respectively, relative to the low symptom profile, and membership in the PTSD profile was associated with younger age, OR = 0.91, relative to the low symptom profile. The CPTSD profile showed the highest level of posttraumatic maladaptive beliefs. This study yields empirical support for the ICD-11 CPTSD model in prison staff. The results provide additional support for the validity of ITQ measurement of PTSD and CPTSD.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".