The association between psychotic-like experiences and violent behavior in adolescents: a cross-lagged longitudinal study
Bibliographic record
Abstract
Psychotic-like experiences (PLEs) have been identified as risk factors for mental health issues and behavioral problems including violence. While cross-sectional studies suggest an association between PLEs and violent behavior in adolescents, their longitudinal relationship remains unexamined. This study aims to examine the temporal association between PLEs and violent behavior in adolescents. PLEs and violent behavior were assessed using data from self-report surveys conducted from 2011 to 2019 in a Tokyo junior and senior high school (grades 7-12). The study included 1685 participants aged 12-18 surveyed annually for up to 6 years. Random intercept cross-lagged panel models (RI-CLPMs) were used to examine between-person and within-person associations among study variables, with analyses stratified by gender. Results showed a bidirectional relationship between PLEs and violent behavior on both the between-person (β = 0.23, p < 0.001) and within-person levels (β = 0.07-0.25, p < 0.05). This relationship was significant for PLEs and violence towards objects (between-person: β = 0.22, p < 0.001; within-person: β = 0.07-0.32, p < 0.05), but not for PLEs and interpersonal violence. When analyzed by gender, these associations were significant in girls but not in boys. The findings suggested that PLEs may have a bidirectional relationship with violent behavior (especially violence towards objects), particularly in girls, indicating potential gender-specific pathways in this association. Further research should explore the underlying mechanisms of this bidirectional relationship, with a focus on gender-specific factors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".