Do we really need two sessions?: The use of a structured interview as a trauma cue reactivity paradigm
Bibliographic record
Abstract
OBJECTIVES: Derived from classical conditioning theory and rooted in motivational mechanisms, cue reactivity paradigms (CRPs) are used in addictions research to measure participants' propensities for substance-relevant responses (e.g., craving) during exposure to substance-relevant cues (e.g., drug paraphernalia). CRPs are also useful in PTSD-addiction comorbidity research, allowing the study of affective and substance-relevant responses to trauma cues. However, studies using traditional CRPs are time-consuming with high attrition rates due to repeat testing. Thus, we sought to test whether a single session semi-structured trauma interview could serve as a CRP in terms of eliciting theorized cue exposure effects on craving and affect measures. METHOD: Fifty regular cannabis users with trauma histories provided detailed descriptions of their most traumatic lifetime experience, and a neutral experience, according to an established interview protocol. Linear mixed models examined the effect of cue type (trauma vs. neutral) on affective and craving responses. RESULTS: As hypothesized, the trauma interview elicited significantly greater cannabis craving (and alcohol craving among the drinkers), and, greater negative affect among those with more severe PTSD symptoms, compared to the neutral interview. CONCLUSION: Results suggest an established semi-structured interview may function effectively as a CRP for use in trauma and addictions research.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".