Expanding and Clarifying the Role of Emotion Regulation in Nonsuicidal Self-Injury
Bibliographic record
Abstract
OBJECTIVE: Deficits in emotion regulation have been implicated in nonsuicidal self-injury (NSSI) by both theory and research. Research indicates that NSSI is commonly performed as an emotion regulation strategy, as it often decreases the experience of negative affect. People who engage in NSSI often report greater emotion dysregulation than those without an NSSI history. Further, interventions that have demonstrated effectiveness in reducing NSSI involve a focus on emotion regulation skills. Given the important role of emotion regulation in NSSI, research should continue to develop our understanding of this construct. METHODS: We conducted a review of relevant research in emotion regulation and dysregulation and specific emotion regulation strategies in NSSI. RESULTS: First, we provide an overview of current research on emotion regulation and dysregulation in NSSI. Second, we discuss the application of a specific emotion regulation model to NSSI research, and review research on NSSI supporting the use of this model. CONCLUSION: NSSI has been associated with an emotion regulation function and trait emotion dysregulation among people who self-injure. Relevant research provides initial support for the applicability of a specific model of emotion regulation to NSSI. We suggest directions for future research to continue to cultivate our understanding of emotion regulation in NSSI.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".