Lifetime traumatic stressors and adverse childhood experiences uniquely predict concurrent PTSD, complex PTSD, and dissociative subtype of PTSD symptoms whereas recent adult non-traumatic stressors do not: results from an online survey study
Bibliographic record
Abstract
This retrospective survey study compared the differential risk of lifetime traumatic stressors, so-called "non-traumatic stressors" experienced over the past year, referring to life events that do not meet the criteria for A1 traumatic events, and adverse childhood experiences (ACE) on severity of DSM-5 versus ICD-11 PTSD, Complex PTSD (CPTSD), and dissociative subtype of PTSD (D-PTSD) symptoms among 418 participants recruited online. In pairwise analyses, all stress types were associated with all outcomes. However, multiple regression and factor analyses indicated that whereas the number of different lifetime traumatic events participants reported experiencing, together with the number of ACE participants experienced, uniquely predicted DSM-5 PTSD, D-PTSD and ICD-11 PTSD and CPTSD symptoms, the number of non-traumatic stressors they experienced during the last year did not. Moreover, ACE uniquely predicted all outcomes even after accounting for lifetime traumatic stress. These results provide further support for the particularly high risk of lifetime traumatic stressors and ACE in predicting trauma and stressor-related symptoms. Future research directions 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".