Mentalization in female adolescents with non-suicidal self-injury and alexithymic and depressive features in their parents
Bibliographic record
Abstract
BACKGROUND: Non-suicidal self-injury (NSSI) is a condition with debilitating consequences. We aimed to assess the mentalization skills of female adolescents with NSSI and parents who showed alexithymia and depressive symptoms. METHOD: Ours was a case-control study. Thirty adolescents with NSSI were recruited into the case group, 31 adolescents were recruited into the control group. Reading the Mind in the Eyes Test (RMET) and the Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children - Present and Lifetime Version (K-SADS-PL) were applied. The Inventory of Statements about Self-Injury was used. The Toronto Alexithymia Scale (TAS-20) and Beck Depression Inventory (BDI) were given to parents. RESULTS: There were no significant differences between two groups for RMET and parental TAS-20 scores. Maternal BDI scores were found to be significantly higher in the NSSI group. There were no significant differences for paternal BDI. RMET scores correlated negatively with maternal BDI scores. Major depression was found to be the most common diagnosis in the NSSI group. CONCLUSION: Because maternal depressive features seem to be related to NSSI, a detailed psychiatric examination of mothers should be carried out. Studies with larger samples or different designs are needed for a better understanding of the mentalization in NSSI.
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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 | 0.001 |
| 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) | 0.002 | 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".