Risk of violent behaviour in young people at clinical high risk for psychosis from the North American Prodrome Longitudinal Studies consortium
Bibliographic record
Abstract
AIM: Although violent behaviour has been studied in schizophrenia, violence risk has received little attention in individuals at clinical high risk for psychosis (CHR). This manuscript aims to report and discuss the overall results of the Structured Assessment for Violence Risk in Youth (SAVRY) from the NAPLS-3 project to explore the risk of violence in CHR youth and to determine the relationship between SAVRY violence risk scores, psychosis risk symptoms, and global functioning. We hypothesized that CHR young people are at higher risk of violence as compared to healthy comparison participants due to a similarity between risk factors for psychosis and risk factors for violence, and that this risk is associated with greater severity of symptoms, poor functioning, and risk for conversion to psychosis. METHODS: Participants from the North American Prodrome Longitudinal Study consortium phase 3 (NAPLS-3) included 684 CHR and 96 HC. Assessments included the Structural Assessment of Violence Risk in Youth (SAVRY), clinical and functional measures. RESULTS: The majority of participants across groups were deemed to be at low risk for violence. There were significantly more CHR participants (29.8%) who had moderate or high scores on the SAVRY Summary Risk Rating compared to HC participants (3.1%). Low versus moderate-high SAVRY scores were associated with better social (p < .005) and role (p < .002) functioning and fewer positive (p < .002), negative (p < .002), disorganized (p < .01) and general symptoms (p < .002). CHR participants with higher SAVRY scores were more likely to be diagnosed with borderline personality disorder, ADHD and substance misuse. Among CHR, overall violence risk was not associated with conversion to psychosis. However, those who converted to psychosis scored lower on the protective factors index, primarily driven by less prosocial involvement and fewer resilient personality traits. CONCLUSIONS: This is the first study to assess violence risk in CHR adolescents. Violence risk factors overlap with risk factors for psychosis in general, perhaps accounting for the association. These findings have implications for intervention efforts to reduce violence risk and bolster resiliency in CHR youth.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".