Expressive Writing About One's Trauma Increases Accessibility of Cannabis Information in Memory Among Trauma-Exposed Individuals
Bibliographic record
Abstract
Objective: Trauma survivors are more likely than others to use cannabis, and post-traumatic stress disorder (PTSD) commonly co-occurs with cannabis use disorder (CUD). Automatic memory associations between trauma reminders and cannabis use have been suggested as contributing mechanisms. These associations can be studied experimentally by manipulating trauma cue exposure in a cue-reactivity paradigm (CRP) and examining effects on the accessibility of cannabis information in memory in trauma survivors with and without PTSD. Method: Cannabis users with trauma histories (N = 202) completed a PTSD measure (PTSD Checklist-5) and were randomized to a trauma or neutral expressive writing task as an online CRP. Next, participants completed a cue-behavior word association task, which involved presentation of a series of ambiguous cue words to which participants provided the first word that came to mind. Some of these ambiguous cues pertained to cannabis (e.g., weed, pot) and some to other substances (e.g., blow, shot). This task was scored by two independent raters. Linear regression models tested the hypothesized main and interactive effects of CRP condition (trauma, neutral) and PTSD group (probable PTSD, no PTSD) on the number of cannabis and other substance responses generated. Results: Main effects of CRP condition were found for cannabis responses (b = 0.41, p = 0.048; trauma > neutral) but not other substance responses. Unexpectedly, no main effects or interactions of PTSD group were observed for either outcome. Conclusions: In cannabis users with trauma histories, writing about one’s trauma specifically activates greater accessibility of cannabis-related information in memory, regardless of PTSD.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".