Relationship between alexithymia, loneliness, resilience and non-suicidal self-injury in adolescents with depression: a multi-center study
Bibliographic record
Abstract
OBJECTIVE: Non-suicidal self-injury (NSSI) behaviors are prevalent in adolescents and have adverse effects on physical and mental health. However, little is known about the relationship between NSSI and alexithymia, or the underlying mechanisms that could explain this relationship. This study aimed to elucidate the current status of NSSI in adolescent depression, and analyze the relationship between alexithymia, loneliness, resilience, and adolescent depression with NSSI, so as to provide a theoretical basis for psychotherapeutic interventions. METHOD: The study sample involved inpatients and outpatients from 12 hospitals across China and adolescents with depression who met the DSM-5 diagnostic criteria for depression episode. The following scales were used: The Functional Assessment of Self-Mutilation, Toronto Alexithymia Scale, UCLA Loneliness Scale, and Connor Davidson Resilience Scale. RESULTS: The detection rate of NSSI in adolescents with depression from 2021.01.01-2022.01.01 was 76.06% (1782/2343). Spearman's correlation analysis revealed a significant correlation between alexithymia, loneliness, resilience and NSSI in depressed adolescents, and the results of the non-parametric test showed that the differences between the two groups for each factor were statistically significant. Binary logistic regression results showed that alexithymia (B = 0.023, p = 0.003, OR = 1.023, 95% CI: 1.008-1.038) and depression (B = 0.045, p < 0.001, OR = 1.046, 95% CI: 1.026-1.066) are risk factors for NSSI, resilience (B = - 0.052, p < 0.001, OR = 0.949, 95% CI: 0.935 - 0.964) is a protective factor for NSSI. Alexithymia directly predicted NSSI and also indirectly influenced NSSI through the mediated effect of resilience. Loneliness moderates the first half of the path of this mediated model. CONCLUSION: The present study confirms a moderated mediation effect: Alexithymia can have an impact on NSSI behaviors in depressed adolescents through the mediating role of resilience. Loneliness, as a moderating variable, moderated the first half of the pathway of the mediating model. We discuss perspectives for future research and interventions based on the findings of the study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".