Interpersonal Trauma and Substance Use Severity: The Serial Mediation of Emotional Intolerance and Emotional Dysregulation
Bibliographic record
Abstract
Substance use is highly prevalent in those with trauma histories, especially in women, which may be in part explained by high rates of interpersonal trauma in this population. Research examining the potential mechanisms underlying the relationship between co-occurring interpersonal trauma histories and substance use disorders (SUDs) is in its infancy. The current study examined whether the relationship between interpersonal trauma and SUD severity could be understood via the sequential ordering of two transdiagnostic emotional vulnerability factors: 1) emotional intolerance (anxiety sensitivity, distress intolerance), and 2) emotional dysregulation (negative urgency, lack of clarity, nonacceptance, limited strategies, difficulties with goal-directed behavior). A sample of 130 adult community-based women self-identifying as experiencing substance use problems completed the online survey. Mediation analyses suggest that as women's lifetime interpersonal trauma increases, so does their SUD severity by way of emotional intolerance and subsequent difficulties regulating their emotions. The findings suggests that transdiagnostic interventions targeting tolerance of aversive emotions may facilitate the ability to learn and employ healthy emotion regulation strategies among women with interpersonal trauma histories and SUDs.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".