The Association of Borderline Personality Features and Self-Injury Among Adolescents with Non-Suicidal Self-Injury: The Mediating Role of Alexithymia
Bibliographic record
Abstract
Introduction: Non-suicidal self-injury (NSSI) is becoming an increasingly prevalent phenomenon among adolescents, endangering their health. The aims of this study were to 1) explore the associations between borderline personality features, alexithymia and NSSI and 2) examine if alexithymia mediates the relationships between borderline personality features and both the severity of NSSI and the various functions that maintain NSSI in adolescents. Methods: This cross-sectional study recruited 1779 outpatient and inpatient aged 12– 18 years from psychiatric hospitals. All adolescents completed a structured four-part questionnaire including demographic items, the Chinese version of the Functional Assessment of Self-Mutilation, the Borderline Personality Features Scale for Children and the Toronto Alexithymia Scale. Results: The structural equation modelling results indicated that alexithymia partially mediated the associations between borderline personality features and both the severity of NSSI and the emotion regulation function of NSSI ( B = 0.058 and 0.099, both p < 0.001), after controlling for age and sex. Discussion: These findings suggest that alexithymia may play a role in the mechanism and treatment of NSSI among adolescents with borderline personality features. Further longitudinal studies are essential to validate these findings. Keywords: self-harm, health, emotion regulation, structural equation modelling
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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) | 0.002 | 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".