Emotion regulation in substance use disorders: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: The ability to regulate emotions effectively has been associated with resilience to psychopathology. Individuals with substance use disorders (SUDs) have been shown to have higher levels of negative emotionality, with some evidence suggesting impairment in emotion regulation compared with individuals without SUDs. However, no previous attempt has been made to systematically review the literature to assess the magnitude of this difference. We aimed to assess the association between SUD diagnosis and emotion regulation as measured by the Difficulties in Emotion Regulation Scale (DERS) and Emotion Regulation Questionnaire (ERQ) through a systematic review and meta-analysis of existing findings. METHODS: The systematic review was conducted using PubMed, PsycINFO and Embase. We examined cross-sectional studies that compared a SUD group with a control group and measured emotion regulation using the DERS or the ERQ. The primary analysis focused on papers using the DERS, as this was the predominant instrument in the literature. RESULTS: Twenty-two studies met our primary analysis criteria, representing 1936 individuals with a SUD and 1567 controls. Individuals with SUDs relative to controls had significantly greater DERS scores, with a mean difference of 21.44 [95% confidence interval (CI) = 16.49-26.40, P < 0.001] and Hedges' g = 1.05 (95% CI = 0.86-1.24, P < 0.001). The difference was robust, remaining significant after removing outliers and studies with high risk of bias. Individuals with SUDs demonstrated poorer emotion regulation on each subscale of the DERS, with the largest deficits in the Strategies and Impulse subscales. The ERQ analysis revealed greater use of expressive suppression in those with SUDs relative to controls (Hedges' g = 0.76, 95% CI = 0.25-1.28, P = 0.004). CONCLUSIONS: People with substance use disorders appear to have greater difficulties in emotion regulation than people without substance use disorders.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".