This article examines which variables predict disengagement from legal proceedings by victims of intimate partner violence in the first steps of the Spanish judicial process. We replicated a previous retrospective study with a prospective sample of 393 women. The relationships of sociodemographic, emotional, motivational, and psychological variables with procedural withdrawals were analyzed. We developed a binary logistic regression model that predicts disengagement with two variables: the contact with the abuser and the interaction between this contact and the thought of going back with him. Interesting differences between the current and the retrospective study were found. Results are discussed extensively in the conclusions.

In order to end and “liberate” themselves from an abusive relationship, female survivors of intimate partner violence (IPV) usually face a complex process. Although women may decide to seek help through the criminal justice system, some refuse to participate in legal proceedings against their abusers. While many studies have focused on exploring variables explaining disengagement from legal proceedings, the aim of this article is to study the impact of the process of liberation from an abusive relationship on the likelihood of disengagement (LoD) from legal proceedings. Liberation was measured through the psychosocial separation overall score and the LoD was predicted by a logistic regression model developed in a previous study in Spain. A sample of 80 women involved in legal proceedings for IPV against their ex-partners in Andalusia (Spain) participated in this study. Exploratory analyses were conducted using ANOVA and Chi-square; multiple linear regression analyses were used to study the relationship between psychosocial separation and LoD. Results showed that victims who had higher psychosocial separation from their abusers were less likely to disengage from legal proceedings against the abuser. We discuss the results in terms of practical implications like detection of women’s need for specific psychological support to ease a comprehensive recovery. Training programs for legal professionals and judges in the judicial arena should use the results of this study to increase professionals’ understanding of IPV and survivors’ decision-making processes. This would lead to a decrease in survivors’ secondary victimization, as well as decrease the frustration of legal professionals when victims disengage from legal proceedings.

Intimate partner violence (IPV) is an actual social issue which poses a challenge in terms of prevention, legal action, and reporting the abuse once it has occurred. In this last case, out of the total of female victims that fill a complaint against their abuser and initiate the legal proceedings, a significant number withdraw from it for different reasons. In this field, it is interesting to detect the victims that disengage from the legal process so that professionals can intervene before it occurs. Previous studies have applied statistical models to use input variables and make a prediction of withdrawal. However, it has not been found in the literature any study that uses machine learning models to predict disengagement from the legal proceedings in IPV cases, which can be a better option to detect these events with a higher precision. Therefore, in this work, a novel application of machine learning techniques to predict the decision of victims of IPV to withdraw from prosecution is studied. For this purpose, three different ML algorithms have been optimized and tested with the original dataset to prove the great performance of ML models against non-linear input data. Once the best models have been obtained, explainable artificial intelligence (xAI) techniques have been applied to search for the most informative input features and reduce the original dataset to the most important variables. Finally, these results have been compared to those obtained in the previous work that used statistical techniques, and the set of most informative parameters has been combined with the variables of the previous study, showing that ML-based models have a better predictive accuracy in all cases and that by adding one new variable to the previous work' subset, the accuracy to detect withdrawal improves by 7.5%.

Intimate partner violence against women (IPVW) is a pressing social issue which poses a challenge in terms of prevention, legal action, and reporting the abuse once it has occurred. However, a significant number of female victims who file a complaint against their abuser and initiate legal proceedings, subsequently, withdraw charges for different reasons. Research in this field has been focusing on identifying the factors underlying women victims’ decision to disengage from the legal process to enable intervention before this occurs. Previous studies have applied statistical models to use input variables and make a prediction of withdrawal. However, none have used machine learning models to predict disengagement from legal proceedings in IPVW cases. This could represent a more accurate way of detecting these events. This study applied machine learning (ML) techniques to predict the decision of IPVW victims to withdraw from prosecution. Three different ML algorithms were optimized and tested with the original dataset to assess the performance of ML models against non-linear input data. Once the best models had been obtained, explainable artificial intelligence (xAI) techniques were applied to search for the most informative input features and reduce the original dataset to the most important variables. Finally, these results were compared to those obtained in the previous work that used statistical techniques, and the set of most informative parameters was combined with the variables of the previous study, showing that ML-based models had a better predictive accuracy in all cases and that by adding one new variable to the previous work’s predictive model, the accuracy to detect withdrawal improved by 7.5%.