Self-harming behaviors among forensic psychiatric patients who committed violent offences: an exploratory study on the role of circumstances during the index offence and victim characteristics
Bibliographic record
Abstract
BACKGROUND: Self-harming behaviors are common among forensic patients with violent index offenses. While various factors, including feelings of shame and guilt, may influence self-harming behaviors, little is known about how the circumstances surrounding the index offense and the victims' characteristics affect self-harming tendencies among forensic patients. In this study, we examined the association of the circumstances surrounding the index offence and victim characteristics with self-harming behaviors among forensic patients who have committed violent offences. METHODS: The present study consisted of 845 forensic psychiatric patients under the Ontario Review Board who had violent offences (Mean age = 42.13 ± 13.29; 85.68% male) in the reporting year 2014/15. The study examined the association between self-harming incidents with the circumstances during the index offense and victims' characteristics while controlling for clinical and demographic factors based on multiple hierarchical negative binominal regression. RESULTS: The prevalence of self-harm was 4.14%, and more than half (61.29%) of the patients with self-harming behaviors had multiple incidents. The total number of self-harming incidences recorded in the reporting year was 113. The results showed that of the overall 24.05% explained by the models, the victim's characteristics contributed approximately 5% points, and circumstances during the index offence contributed an additional 2% points in explaining self-harming behaviors among forensic psychiatric patients during the reporting year. In the final model, the risk of self-harm increased with having a victim who was a healthcare/support staff or a co-patient/cohabitant. CONCLUSION: Self-harm among forensic patients who committed violent offences is associated with various factors, including previous history of self-harm and the victim's characteristics, especially when the victim was a healthcare/support worker or co-patient. These findings suggest that self-harm might be a maladaptive way of coping with negative emotions, such as feelings of guilt and shame triggered by harming others. Mitigating measures for self-harm among patients with violent offences need to be robust and individualized, taking into consideration vulnerability issues and the best available evidence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".