Effects of Trauma Cue Exposure and Posttraumatic Stress Disorder (PTSD) on Affect and Cannabis Craving in Cannabis Users With Trauma Histories: Use of Expressive Writing as an Online Cue-Reactivity Paradigm: Effets de l'exposition aux signaux traumatiques et du SSPT sur l'affect et le besoin de cannabis chez les consommateurs de cannabis ayant des antécédents de traumatismes : utilisation de l'écriture expressive comme paradigme de réactivité en ligne
Bibliographic record
Abstract
OBJECTIVES: Posttraumatic stress disorder (PTSD) and cannabis use disorder (CUD) commonly co-occur. Conditioned associations between psychological trauma cues, distress, cannabis use, and desired relief outcomes may contribute to the comorbidity. These conditioned associations can be studied experimentally by manipulating trauma cue exposure in a cue-reactivity paradigm (CRP) and examining effects on affective and cognitive outcomes in participants with and without PTSD. However, traditional CRPs take place in-lab limiting recruitment/power. We aimed to examine the effects of CRP condition (trauma and neutral) and PTSD group (likely PTSD+ and PTSD-) on affective and craving outcomes using a stand-alone online expressive writing CRP. METHODS: = 14.71) with psychological trauma histories and past-month cannabis use completed a measure of PTSD symptoms (PTSD Checklist-5 for DSM-5 [PCL-5]) and were randomized to complete either a trauma or neutral expressive writing task. Then they completed validated measures of affect (Positive and Negative Affect Schedule-Short Form [PANAS-SF]) and cannabis craving (Marijuana Craving Questionnaire-Short Form [MCQ-SF]). RESULTS: Linear mixed models tested the hypothesized main and interactive effects of CRP condition (trauma and neutral) and PTSD group (likely PTSD+ and PTSD-) on negative and positive affect (PANAS-SF) and cannabis craving dimensions (MCQ-SF). The hypothesized main effects of trauma versus neutral expressive writing were found for negative affect and the expectancy dimension of cannabis craving and of PTSD group for negative affect and all cannabis craving dimensions; no interactions were observed. CONCLUSIONS: Expressive writing appears a useful online CRP. Interventions focused on reducing negative affect and expectancy craving to trauma cues may prevent/treat CUD among cannabis users with PTSD. PLAIN LANGUAGE SUMMARY TITLE: The Use of an Online Expressive Writing as a Trauma Cue Exposure: Effects on Craving and Emotions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".