Examination of the structural relations between posttraumatic stress disorder symptoms and reckless/self-destructive behaviors.
Bibliographic record
Abstract
Posttraumatic stress disorder (PTSD) symptoms commonly co-occur with reckless and self-destructive behaviors (RSDBs; e.g., substance use, aggression). To better understand comorbidity mechanisms between RSDBs and PTSD symptom clusters (best-fitting PTSD model), this study examined their latent-level relations. Methodologically, the current study used a cross-sectional approach administering self-report surveys (PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, measuring PTSD severity and the Posttrauma Risky Behaviors Questionnaire measuring RSDBs) to a convenience sample. The study description (45–60 min survey to develop a posttrauma reckless behaviors measure), compensation, and eligibility information was posted on Amazon’s Mechanical Turk platform. A sample of 417 trauma-exposed community participants averaging 35.92 years of age (56.60% female) was recruited. Confirmatory factor analyses revealed that the seven-factor PTSD hybrid model provided optimal fit to the data. Wald χ2 tests of parameter constraint results indicated the strongest relation of the RSDB factor with PTSD’s Externalizing Behaviors factor (r = .70) and weakest relation with PTSD’s Avoidance factor (r = .37); PTSD’s Anhedonia factor (r = .53) had a stronger relation to the RSDB factor compared with PTSD’s Anxious Arousal factor (r = .43). Results support the construct validity of the PTSD hybrid model factors in relation to RSDBs. Additionally, results indicate that PTSD’s Positive Affect factor may be strongly embedded in the PTSD–RSDB relation, supporting the emotion dysregulation viewpoint and trauma interventions addressing emotion dysregulation (including for positive emotions). Lastly, our study results provide additional psychometric support for the Posttrauma Risky Behaviors Questionnaire. (PsycINFO Database Record (c) 2020 APA, all rights reserved)
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".