An international comparison of adolescent non-suicidal self-injury (NSSI) and suicide attempts: Germany and the USA
Bibliographic record
Abstract
BACKGROUND: This study examined the prevalence of non-suicidal self-injury (NSSI), suicide attempts, suicide threats and suicidal ideation in a German school sample and compared the rates with a similar sample of adolescents from the midwestern USA by using cross-nationally validated assessment tools. METHOD: Data were provided from 665 adolescents (mean age 14.8 years, S.D.=0.66, range 14-17 years) in a school setting. Students completed the Self-Harm Behavior Questionnaire (SHBQ), the Ottawa Self-Injury Inventory (OSI) and a German version of the Center for Epidemiological Studies-Depression Scale (CES-D). RESULTS: A quarter of the participants (25.6%) endorsed at least one act of NSSI in their life, and 9.5% of those students answered that they had hurt themselves repetitively (more than four times). Forty-three (6.5%) of the students reported a history of a suicide attempt. No statistically significant differences were observed between the German and US samples in terms of self-injury or suicidal behaviors. CONCLUSIONS: By using the same validated assessment tools, no differences were found in the prevalence and characteristics of self-injury and suicidal behaviors between adolescents from Germany and the USA. Thus, it seems that NSSI has to be understood as worldwide phenomenon, at least in Western cultures.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".