Effects of Developmental Abuse and Symptom Suppression among Traumatized Veterans
Bibliographic record
Abstract
While much of the research on military posttraumatic stress disorder (PTSD) focuses on warzone reactions, a burgeoning literature highlights complex relationships between childhood adversity and adult-onset PTSD. However, conceptual efforts to delineate the effects of childhood abuse on treatment trajectories for traumatized military veterans are lacking. This study compared trauma and psychological symptom profiles for developmentally abused and non-abused Canadian Forces (CF) veterans (N = 108) diagnosed with operational PTSD. Subscale scores from the Detailed Assessment of PTSD Scale (DAPS) and the Personality Assessment Inventory (PAI) were submitted to MANOVA. The analysis resulted in a composite variable reflecting’ symptom suppression efforts’ that separated abused veterans (n = 55) from non-abused veterans (n = 53). Post hoc analyses showed significant differences between the abused sub-groups (i.e., physical and sexual abuse [n = 15]; physical abuse only [n = 17]; sexual abuse only [n = 23]) and the non-abused group. Veterans with abuse histories had higher symptom suppression scores, reflecting higher levels of substance abuse, post-traumatic dissociation, interpersonal mistrust, as well as, lower depression and PTSD impairment scores. Implications for clinicians and an alternative intervention for treating traumatized military personnel with histories of developmental abuse are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".