Effects of Violence Trajectories on Treatment Outcomes in Schizophrenia Spectrum Disorders
Bibliographic record
Abstract
INTRODUCTION: The relationship between violence and schizophrenia spectrum disorders (SSDs) is complex and poorly understood. Moreover, violence takes different trajectories, depending on its onset relative to that of the illness. However, the effect of such trajectories on the illness is not fully understood in nonforensic populations. METHODS: Two hundred twenty-three participants with SSD were recruited and divided into different violence subgroups using the Brown-Goodwin scale. Psychotic, affective, cognitive, and functional outcomes were measured. RESULTS: Subgroups only significantly differed in psychotic outcomes, such as paranoia (p=.044), measured by the Symptoms Checklist Scores (SCL-90). Pair-wise analysis revealed that those with childhood and adulthood violence displayed significantly higher paranoia, compared with the nonviolent group (p=.015). However, this was not significant after correcting for multiple comparisons. CONCLUSIONS: Different violence trajectories are associated with different symptomatic outcomes in SSD. This suggests an interplay between violence and psychosis, which is important for comprehensive treatment approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".