Posttraumatic Stress Disorder Symptoms and Coping Motives are Independently Associated with Cannabis Craving Elicited by Trauma Cues
Bibliographic record
Abstract
Cannabis use is common among individuals with posttraumatic stress disorder (PTSD), although its use can ultimately worsen PTSD outcomes. Cannabis-use coping motives may help explain the PTSD-cannabis relationship. Frequent pairing of trauma cues with substance use to cope with negative affect can lead to conditioned substance craving. For the present cue-reactivity study, we examined if PTSD symptoms were associated with cannabis craving elicited by a personalized trauma cue and explored whether coping motives mediated this hypothesized relationship; enhancement motives were included as a comparison mediator. Participants (N = 51) were trauma-exposed cannabis users who completed validated assessments on PTSD symptom severity and cannabis use motives. They were then exposed to a personalized audiovisual cue based on their own traumatic experience after which they responded to questions on a standardized measure regarding their cannabis craving. The results demonstrated that PTSD symptoms were associated with increased cannabis craving following trauma cue exposure, B = 0.43, p = .004, 95% CI [0.14, 0.72]. However, the results did not support our hypothesis of an indirect effect through general coping motives, indirect effect = .03, SE = .08, 95% CI [-.10, .21]. We found an independent main effect of general coping motives on trauma cue-elicited cannabis craving, B = 1.86, p = .002, 95% CI [0.72, 3.01]. These findings have important clinical implications suggesting that clinicians should target both PTSD symptoms and general coping motives to prevent the development of conditioned cannabis craving to trauma reminders in trauma-exposed cannabis users.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".