Which Emotion Regulation Strategies are Most Associated with Trait Emotion Dysregulation? A Transdiagnostic Examination
Bibliographic record
Abstract
Although definitions of emotion dysregulation infer difficulties in selecting and implementing emotion regulation (ER) strategies, surprisingly few studies have examined the relationship between trait emotion dysregulation and a wide range of specific ER strategies. The present study used a data-driven approach to assess trait- and state-related ER strategy use in 99 women (aged 18-55) recruited from the community with varying levels of trait emotion dysregulation. Participants completed self-report questionnaires assessing habitual ER strategy implementation and self-ratings of ER strategy use in vivo during negative mood inductions. Principal components analysis revealed four self-report questionnaire-based and three mood-induction-based groupings comprising both optimal and suboptimal strategies. After adjusting for demographic and clinical variables, results from self-report questionnaires indicated that trait emotion dysregulation was significantly associated with higher endorsements of suboptimal strategies in two groupings (e.g., self-criticism, rumination, and social withdrawal; catastrophizing and blaming others) and lower endorsements of optimal ER strategies in one grouping (e.g., cognitive reappraisal and problem solving). In the context of mood induction, trait emotion dysregulation was significantly associated with higher endorsements of suboptimal ER strategies from one cluster only (e.g., expressive suppression, thought avoidance, and self-criticism). Such transdiagnostic, data-driven approaches can uncover how the application of specific ER strategies both habitually and during negative mood states is associated with trait emotion dysregulation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".