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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».