Assessing Clinical Features of Adolescents Suffering from Depression Who Engage in Non-Suicidal Self-Injury
Bibliographic record
Abstract
Depressive disorders (DDs) and non-suicidal self-injury (NSSI) are important juvenile mental health issues, showing alarming increasing rates. They frequently co-occur, mainly among adolescents, increasing the suicide risk. We aimed to compare the clinical features of two groups of adolescents with DDs, differed by their engagement or not in NSSI (“DD + NSSI” and “DD”). We hypothesized that NSSI would characterize particularly severe forms of DDs suitable for becoming specific phenotypes of adolescent depression. We enrolled 56 adolescents (11–17 years) diagnosed with a DD according to the DSM-5 criteria. They were assessed for NSSI endorsement (Ottawa Self-Injury Inventory), depressive symptoms (Children’s Depression Inventory 2), emotional dysregulation (Difficulties in Emotional Regulation Scale), and anxiety symptoms (Screen for Child Anxiety-Related Emotional Disorders). The two groups accounted for 31 (“DD + NSSI”) and 25 (“DD”) individuals. The “DD + NSSI” group had significantly higher suicidal ideation (p 0.0039), emotional dysregulation (p 0.0092), depressive symptoms (p 0.0138), and anxiety symptoms (p 0.0153) than the “DD” group. NSSI seemed to characterize more severe phenotypes of adolescent depression, applying for a potential role as a “specifier” of DDs, describing relevant information for their management. Further studies are needed to support this hypothesis and its potential opportunities for prevention and 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 | 0.001 | 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".