Examining Systemic and Interpersonal Bias in Violence Risk Assessments of Patients in Acute Psychiatric Care
Bibliographic record
Abstract
OBJECTIVE: The assessment and management of inpatient risk for violence in acute psychiatric care are challenges that introduce the potential for bias. This study aimed to examine inequities based on social determinants of health (SDoH) (e.g., race-ethnicity, gender, or mode of admission to acute care) that may lead to unfair assessment of psychiatric patients. METHODS: The authors analyzed electronic health records of 7,424 acute care patients across 12,650 stays (2016-2022) at a large Canadian psychiatric hospital. Risk ratios (RRs) were calculated by SDoH for staff assessments of high risk (perceived risk), for violent incidents (actual risk), and for potentially biased risk assessment (particularly when a patient was assessed as high risk but did not become violent). RESULTS: In univariate analyses, patients assessed as high risk who did not become violent were more likely to be male than female and to be Black, Indigenous, or Middle Eastern than White. When RRs were mutually adjusted for all variables, the associations for gender and race-ethnicity were attenuated or were no longer statistically significant. Associations with potentially biased risks that remained significant included most psychiatric diagnoses (vs. a depressive or anxiety disorder), supportive or unstable housing (vs. owning a home), and admission by police (vs. self-admission; RR=2.14, 95% CI=1.92-2.40). CONCLUSIONS: Systemic factors, such as admission by police and housing status, and having severe mental illness were the primary drivers of observed inequities in risk assessments of patients from racial-ethnic minority groups. Addressing these systemic factors might be key to improving acute psychiatric care.
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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 | 0.048 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".