Do trauma cue exposure and/or PTSD symptom severity intensify selective approach bias toward cannabis cues in regular cannabis users with trauma histories?
Bibliographic record
Abstract
Trauma cue-elicited activation of automatic cannabis-related cognitive biases are theorized to contribute to comorbid posttraumatic stress disorder and cannabis use disorder. This phenomenon can be studied experimentally by combining the trauma cue reactivity paradigm (CRP) with cannabis-related cognitive processing tasks. In this study, we used a computerized cannabis approach-avoidance task (AAT) to assess automatic cannabis (vs. neutral) approach bias following personalized trauma (vs. neutral) CRP exposure. We hypothesized that selective cannabis (vs. neutral) approach biases on the AAT would be larger among participants with higher PTSD symptom severity, particularly following trauma (vs. neutral) cue exposure. We used a within-subjects experimental design with a continuous between-subjects moderator (PTSD symptom severity). Participants were exposed to both a trauma and neutral CRP in random order, completing a cannabis AAT (cannabis vs. neutral stimuli) following each cue exposure. Current cannabis users with histories of psychological trauma (n = 50; 34% male; mean age = 37.8 years) described their most traumatic lifetime event, and a similarly-detailed neutral event, according to an established interview protocol that served as the CRP. As hypothesized, an AAT stimulus type x PTSD symptom severity interaction emerged (p = .042) with approach bias greater to cannabis than neutral stimuli for participants with higher (p = .006), but not lower (p = .36), PTSD symptom severity. Contrasting expectations, the stimulus type x PTSD symptoms effect was not intensified by trauma cue exposure (p = .19). Selective cannabis approach bias may be chronically activated in cannabis users with higher PTSD symptom severity and may serve as an automatic cognitive mechanism to help explain PTSD-CUD co-morbidity.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".