Cumulative trauma and current posttraumatic stress disorder status in general population and inmate samples.
Bibliographic record
Abstract
OBJECTIVE: This research was undertaken to examine the role between cumulative exposure to different types of traumatic events and posttraumatic stress disorder (PTSD) status in general population and prison samples. METHODS: Two archival datasets were examined: the standardization sample for the Detailed Assessment of Posttraumatic States (DAPS; Briere, 2001), and data from a study on trauma and posttraumatic sequelae among inmates and others. RESULTS: PTSD was found in 4% of the general population sample and 48% of the prison sample. Trauma exposure was very common among prisoners, including a 70% rate of childhood sexual abuse for women and a 50% rate for men. Lifetime number of different types of trauma was associated with PTSD in both the general population and prison samples, even when controlling for the effects of sexual trauma. Cumulative interpersonal trauma predicted PTSD, whereas cumulative noninterpersonal trauma did not. In the general population sample, participants who had only 1 type of trauma exposure had a 0% likelihood of current PTSD, whereas those with 6 or more other trauma types had a 12% likelihood. In the prison sample, those with only 1 type of trauma exposure had a 17% percent likelihood of current PTSD, whereas those exposed to 6 or more other trauma types had a 64% chance of PTSD. CONCLUSION: Cumulative trauma predicts current PTSD in both general population and prison samples, even after controlling for sexual trauma. PTSD appears to develop generally as a function of exposure to multiple types of interpersonal trauma, as opposed to a single traumatic event. (PsycINFO Database Record
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".