Posttraumatic Stress Disorder Symptomatology and Substance Use in an Outpatient Concurrent Disorders Sample
Bibliographic record
Abstract
OBJECTIVE: Posttraumatic stress disorder (PTSD) and substance use disorders (SUDs) present a complex and often severe clinical presentation within a concurrent disorders context. The objective of this study was to examine associations between PTSD symptoms and SUD outcomes to better understand the clinical phenomenon of comorbid PTSD and SUD. Multivariate statistical methods were used to test the hypothesis that elevated PTSD symptoms, both at the level of global severity and specific PTSD symptom clusters, are associated with greater substance use and related problems. METHODS: Data were collected from an intake assessment battery within a specialized concurrent disorders outpatient service in Hamilton, ON. The sample comprised 326 participants (mean age = 37.19, 45.4% female). Structural equation models examined associations between PTSD and alcohol, cannabis, and substance use frequency and problems, controlling for age and sex. Alcohol was ultimately dropped from the model due to non-significant bivariate associations. RESULTS: Higher global PTSD symptomatology was significantly associated with higher cannabis and other substance use frequency and related problems. Analyses using PTSD cluster scores showed higher scores for alterations in arousal were positively associated with cannabis-related problems, drug-related problems, and cannabis and other substance use frequency. Avoidance was significantly associated with cannabis frequency and cannabis-related problems. In general, effect sizes were small in magnitude, accounting for between 9% and 25% of variance. CONCLUSION: Significant cluster-level associations indicate the importance of specific PTSD symptoms (hyperarousal, avoidance) in relation to substance use when identifying therapeutic targets among individuals presenting with comorbid PTSD-SUD. This multivariate approach provides a higher resolution and potentially more clinically informative representation of the complex clinical presentation of PTSD and SUD in a concurrent disorder population and could guide the development of more effective treatment paths.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".