Examining the Indirect Effects of Emotion Dysregulation between Interpersonal Trauma Endorsement and Impulsivity Facets
Bibliographic record
Abstract
Objectives. Impulsivity is a common reaction following interpersonal trauma (IPT) experiences, such as physical and sexual assault. Research suggests emotion dysregulation (ED) explains the link between IPT and impulsivity. To advance this research, we assessed the indirect effects of ED on associations between physical/sexual assault endorsement and the five impulsivity facets (negative urgency, positive urgency, lack of premeditation, lack of perseverance, sensation seeking). Methods. A sample of 176 participants seeking treatment at a community mental health center [M age = 34.79; women = 52.8%] completed the scales on traumatic experiences, ED, and the five impulsivity facets. Five simple mediation models were conducted to examine the hypothesized indirect effects on each impulsivity facet. Results. Results revealed significant indirect effects of ED in associations between IPT endorsement and negative urgency (B =2.17, SE =0.58, 95% CI [1.09, 3.35]), positive urgency ( B =1.87, SE =0.59, 95% CI [0.84, 3.16]), lack of premeditation ( B =1.92, SE =0.58, 95% CI [0.89, 3.18]), lack of perseverance ( B =1.67, SE =0.49, 95% CI [0.79, 2.73]), and sensation seeking ( B =0.83, SE =0.36, 95% CI [0.20, 1.62]). Conclusion . ED explained the relationship between IPT endorsement and each of the five facets of impulsivity. IPT survivors may experience more ED, which may increase impulsivity. Findings help identify underlying factors that relate to survivors’ level of impulsivity following IPT experiences, as well as suggest trauma treatments targeting ED may be effective in reducing post-trauma impulsivity for these individuals.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".