A self‐determination theory perspective on the relationship between emotion regulation styles, mindfulness facets, and well‐being in adults with self‐injury
Bibliographic record
Abstract
BACKGROUND: The relevance of emotion regulation (ER) difficulties to nonsuicidal self-injury (NSSI; the deliberate destruction of one's bodily tissue without suicidal intent) has been repeatedly documented. Recently, specific mindfulness facets (i.e., awareness, nonjudging, describing) have been proposed as mechanisms that explain this relationship. The present study sought to extend this line of inquiry by exploring the mediating role of mindfulness facets in the relation between self-determination theory-based ER styles (i.e., integrative ER, suppressive ER, emotion dysregulation) and indices of positive and negative well-being (i.e., subjective vitality, NSSI difficulties), while controlling for gender, in adults with recent NSSI engagement. METHODS: US adults with a history of more than one occurrence of NSSI within the last year (n = 222) completed online measures of ER styles, mindfulness facets, subjective vitality, and NSSI difficulties. RESULTS: A mediation model indicated that the effects of ER styles on positive and negative well-being were explained by specific mindfulness facets (i.e., awareness, nonjudging, nonreactivity, describing). CONCLUSIONS: The present study provides preliminary evidence that facets of dispositional mindfulness may be mechanisms through which ER styles impact positive and negative indices of well-being in adults with lived experience of 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".