Analysis of characteristics of non-suicidal self-injury in 120 adolescents
Bibliographic record
Abstract
ObjectiveTo explore the characteristics of adolescent non-suicidal self-injury （NSSI） behavior， and to enable clinicians to further understand adolescents with NSSI behavior， so as to make better clinical diagnosis and intervention.MethodsFrom July 2022 to June 2023， 120 adolescent patients with NSSI behavior were selected from the outpatient department of our hospital by convenience sampling， and the general demographic data were collected by self-made general situation questionnaire. The characteristics and motivation of NSSI among adolescents with NSSI were analyzed by using the NSSI questionnaire and Ottawa self-injury inventory （OSI）.ResultsThe average age of the first NSSI in the adolescents was 12.90±1.233， and the most common was 13 years old. The most common site of NSSI was the lower arm or wrist （58.33%）， followed by the hand （27.5%）. The most commonly used NSSI was "intentionally cutting oneself" （68.33%）； The most common motivation for NSSI is emotional regulation.ConclusionThere are significant differences in gender among adolescents with NSSI behavior. The age of first NSSI is concentrated， and the highest incidence is in the 11‒14 years old. The common way of self-injury is cutting. NSSI is usually associated with the intention to relieve suffering and is characterized by high frequency， using variety of methods， and low mortality. Adolescents with NSSI often choose to implement NSSI with emotion regulation as the main function， including external emotion regulation and internal emotion regulation.
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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 | 0.001 |
| 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".