Victimization of People With Severe Mental Illness Outside and Within the Mental Health Care System: Results on Prevalence and Risk Factors From a Multicenter Study
Bibliographic record
Abstract
We performed a cross-sectional study using a self-reporting survey to assess lifetime violent and non-violent victimization in people with severe mental illness experienced both inside (i.e., any service providing mental health care such as psychiatric hospitals, psychosocial rehabilitative programs, or outpatient care) and outside (i.e., in the personal life of the participants) of the mental health care system. We recruited 170 participants from 20 community mental health facilities. We built logistic regression models to assess potential risk factors for victimization inside the mental health care system. Outside of the mental health care system, the most commonly reported events were theft (n=93, 54.7%), physical violence without use of a weapon (n=87, 51.2%), and sexual harassment (n=82, 50.6%). Within the mental health care system, most commonly reported incidents were theft (n=68, 40.0%), sexual assault (n=18, 10.6%), and physical violence (n=47, 27.7%) by other patients or staff. Significant risk factors for specific victimization events inside the mental health care system were psychotic disorder, victimization in childhood and youth, female gender, number of hospitalizations, and duration of illness. Findings call for increased attention to victimization of people with severe mental illness, especially within the mental health care system as such victimization events may severely impact patients' trajectories.
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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 | 0.004 |
| 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 | 0.001 |
| 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".