The emotion regulation motive of nonsuicidal self-injury mediates the relationship between motor impulsivity and NSSI frequency in adolescents
Bibliographic record
Abstract
Background: Nonsuicidal self-injury (NSSI) is a common and acute mental health issue among hospitalised adolescents. Although prior research has highlighted the roles of both impulsivity and emotion regulation in self-injurious behaviours, the specific mediating role of the emotion regulation motive in the relationship between motor impulsivity and NSSI frequency remains insufficiently understood. Methods: 206 adolescents with a history of NSSI were recruited from the Affiliated Kangning Hospital of Ningbo University. Subjects filled out the Ottawa Self-Injury Inventory (OSI) to evaluate the frequency and motives of NSSI behaviours, and the Barratt Impulsivity Scale-11 (BIS-11) to assess impulsivity. We conducted a mediation analysis and employed Causal mediation analysis to test whether emotional regulation function mediates the relationship between motor impulsivity and NSSI frequency. Results: < 0.01). Causal mediation analysis revealed that motor impulsivity significantly influenced NSSI frequency through emotion regulation, with no direct effect observed (all ADEs, p > 0.05). Specifically, higher motor impulsivity was linked to increased probabilities of engaging in weekly (ACME = 0.0030, p < 0.001) and daily NSSI (ACME = 0.0017, p < 0.001), while emotion regulation mediated approximately 80% of the total effect. Conclusion: The study demonstrates that higher motor impulsivity is associated with a greater likelihood of engaging in weekly and daily NSSI, with emotion regulation motive significantly mediating this relationship. This highlights the need for interventions targeting impulsivity and emotion regulation to address NSSI behaviours in this population effectively.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".