Emotional Regulation in Substance-Related and Addictive Disorders Treatment: A Systematic Review
Bibliographic record
Abstract
Emotions play an undeniable role in addictive disorders. Given the high relapse and drop-out rates still prevalent in current treatments, it is crucial to explore curative alternatives that take greater account of emotions. The primary objective of this systematic review is to gather literature related to emotion regulation in psychological addictive disorders treatments. The aim is to describe its use for individuals with behavioral (such as gambling disorder, problematic Internet gaming, and Internet addiction) or substance-related disorders. Following a screening of nearly 12,000 articles from six databases and the grey literature, 38 studies that met the selection criteria were included. The results show that 63.2% of the studies had a psychological treatment predominantly based on emotional regulation, with 81.6% (n = 31) of third-wave cognitive-behavioral interventions. The most frequently utilized intervention techniques for emotional regulation were those that facilitated an individual's awareness of their emotional state or provided psychological education to assist in identifying emotions. It would be valuable for future research to explore the most effective content for emotional regulation in treating substance-related and addictive disorders and to determine the specific client population that would benefit the most from this treatment.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".