Antisocial personality disorder and associated factors among incarcerated in prison in Dessie city correctional center, Dessie, Ethiopia: a cross-sectional study

Prediction of Impulsive Aggression Based on Video Images

In response to the subjectivity, low accuracy, and high concealment of existing attack behavior prediction methods, a video-based impulsive aggression prediction method that integrates physiological parameters and facial expression information is proposed. This method uses imaging equipment to capture video and facial expression information containing the subject's face and uses imaging photoplethysmography (IPPG) technology to obtain the subject's heart rate variability parameters. Meanwhile, the ResNet-34 expression recognition model was constructed to obtain the subject's facial expression information. Based on the random forest classification model, the physiological parameters and facial expression information obtained are used to predict individual impulsive aggression. Finally, an impulsive aggression induction experiment was designed to verify the method. The experimental results show that the accuracy of this method for predicting the presence or absence of impulsive aggression was 89.39%. This method proves the feasibility of applying physiological parameters and facial expression information to predict impulsive aggression. This article has important theoretical and practical value for exploring new impulsive aggression prediction methods. It also has significance in safety monitoring in special and public places such as prisons and rehabilitation centers.

Suicidal behavior and associated factors among prisoners at Dessie town correctional institution, Dessie, Ethiopia

BACKGROUND

Suicide is a prominent cause of death among inmates worldwide, accounting for over 30% of all deaths. Several factors, including prison-related, psychiatric disorders, stressful life events, and substance use-related factors are believed to be associated with an increased risk of suicidal behavior in a correctional facility. The present study aimed to determine the prevalence and associated factors of Suicidal Behavior among prisoners at Dessie town correctional institution, Ethiopia.

METHODS

From February 16 to March 5, 2020, a cross-sectional survey was conducted among 288 inmates at the Dessie Town Correctional Center. A systematic random sampling technique was used to select study participants during the study period. Data was collected through face-to-face interview methods using Suicidal Behavior Revised (SBQ-R). The collected data were coded, entered into Epi-data data version 3.1, and analyzed by SPSS Version 26. Binary logistic regression was carried out to identify independent predictors of suicidal behavior at a 95% confidence level. Variables at a p-value < 0.05 with 95% CI were declared statistically significant.

RESULTS

The prevalence of Suicidal behavior among prisoners was found to be 25.3% [(95% CI: 20.5, 30.6)]. This study showed that being female [AOR = 5.14;95% CI (1.62,16.29)], depression [AOR = 4.97;95%CI (2.53,9.77)], anxiety [AOR = 3.14; 95%CI (1.59,6.22)], experienced stressful life events [AOR = 5.11; 95%CI (2.24, 11.65)], and ever substance use [AOR = 2.83; 95%CI (1.41, 5.59)] were variables significantly associated with suicidal behavior among prisoners in Dessie town correctional institution.

CONCLUSION AND RECOMMENDATIONS

In this study, suicidal behavior was highly prevalent among prisoners compared to the general population. Being female, depression, anxiety, stress full life events, and substance use were variables that are independent predictors of suicidal behavior. This study recommends that the institution needs to deliver an appropriate psychiatric facility to diagnose and treat prisoners with suicidal behavior. Also, special attention should be given to early screening and treatment of prisoners through prison health services, which is the most critical prevention strategy of suicide in prisoners.