Predictors of non-suicidal self-injury in adolescents with depressive disorder: the role of alexithymia, childhood trauma, and body investment
Bibliographic record
Abstract
Purpose: This study analyzes the relationship of alexithymia, childhood trauma, and body investment to non-suicidal self-injury (NSSI) behaviors in adolescents with depressive disorder and whether they have predictive and diagnostic value for non-suicidal self-injury (NSSI) behaviors in adolescents with depressive disorder. Patients and methods: A total of 225 patients with a diagnosis of adolescent depressive disorder were included in the study and were divided into two groups according to the DSM-5 criteria: 98 cases without NSSI and 127 cases with NSSI. Compare the demographic data, 24-item Hamilton Depression Scale (HAMD-24), 20-item Toronto Alexithymia Scale (TAS-20), Childhood Trauma Questionnaire-Short Form (CTQ-SF), and Body Investment Scale (BIS) scores between two groups. Binary logistic regression was used to analyze the independent risk factors contributing to NSSI behaviors in adolescents with depression, and establish four predictive models. Based on the models' predictive probability, the ROC curves were plotted to calculate the value of the predictive diagnostic effect. Results: The group without NSSI had lower scores than the group with NSSI on HAMD-24 total score, TAS-20 total score, difficulty identifying feelings, difficulty describing feelings, and externally focused thinking, as well as lower scores on CTQ-SF total score, physical neglect, emotional neglect, physical abuse, and emotional abuse. In contrast, the BIS total score, body image feelings and attitudes, body care, and body protection factor scores were higher for the group without NSSI. The BIS body care factor score and the CTQ-SF emotional abuse factor score were significantly linked with adolescents diagnosed with depressive disorder who exhibited NSSI behaviors. These results provide a good diagnostic model for adolescents with depressive disorder. Conclusion: Low levels of body care and childhood emotional abuse may independently contribute to the implementation of NSSI in adolescents with depressive disorder. Body investment and childhood trauma are valuable in diagnosing and predicting NSSI behaviors and should be considered as potentially important factors in clinical treatment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".