Estimation of the Development of Depression and PTSD in Children Exposed to Sexual Abuse and Development of Decision Support Systems by Using Artificial Intelligence

Gender Differences in the Characterization of Child Sexual Abuse

Research on child sexual abuse (CSA) has increased in recent decades. However, the study of gender differences in this field is still scarce. The aim of this study was to analyze the differences in the characterization of CSA between Spanish adult men and women. The Juvenile Victimization Questionnaire was administered to 162 cisgender victims of CSA aged 18-63. Most of the abuses involved physical contact and were committed by a known person. Twenty percent of the victims indicated that they had developed a mental health problem that they believed was due to the CSA experience. Women suffered more CSA with physical contact and penetration, more types of abuse and more abuse by men, and were abused more frequently by a family member. Compared to women, men suffered more sexual abuse by women. Exploring gender differences in CSA could help to improve preventive strategies and interventions aimed at reducing the occurrence of this type of abuse and sequelae resulting from it.

ChatGPT educates college students about sexual violence and its impact on their mental health: a proposal

ChatGPT can be, as an interactive information platform, used to facilitate learning about the psychological effects of sexual violence. Since its approach is interactive and also that it is easily accessible, it can aid in the dissemination of information, the prevention of sexual violence and its treatment. Further, it can be incorporated into the curriculum to help raise awareness about this delicate subject and aid the affected students.

Toward Strengthening the Capacity of the Health System: A Cross-Sectional Study of Factors Predicting Quality of Life in 545 Nigerian Children Exposed to Sexual Abuse

In Nigeria, the predictors of quality of life among children exposed to sexual abuse are unknown. Addressing this gap may strengthen the capacity of the health system to care for this population. Thus, this cross-sectional study selected 545 (mean age = 14.4 ± 1.4 years) Nigerian children exposed to sexual abuse. Results show that self-compassion, resilience, and meaning in life jointly predicted quality of life and explained 39% variance. The independent prediction of each predictor variable shows that self-compassion, resilience, and meaning in life have significant independent predictions, with self-compassion showing the greatest independent prediction, followed by resilience and meaning in life. Sex, age, and how long ago respondents were exposed to sexual abuse jointly predicted quality of life and explained 6% variance. However, how long ago respondents were exposed to sexual abuse shows a significant independent prediction. Results offer clinical implications that may strengthen the capacity of the health system to care for this population.

Artificial Intelligence and Child Abuse and Neglect: A Systematic Review

All societies should carefully address the child abuse and neglect phenomenon due to its acute and chronic sequelae. Even if artificial intelligence (AI) implementation in this field could be helpful, the state of the art of this implementation is not known. No studies have comprehensively reviewed the types of AI models that have been developed/validated. Furthermore, no indications about the risk of bias in these studies are available. For these reasons, the authors conducted a systematic review of the PubMed database to answer the following questions: "what is the state of the art about the development and/or validation of AI predictive models useful to contrast child abuse and neglect phenomenon?"; "which is the risk of bias of the included articles?". The inclusion criteria were: articles written in English and dated from January 1985 to 31 March 2023; publications that used a medical and/or protective service dataset to develop and/or validate AI prediction models. The reviewers screened 413 articles. Among them, seven papers were included. Their analysis showed that: the types of input data were heterogeneous; artificial neural networks, convolutional neural networks, and natural language processing were used; the datasets had a median size of 2600 cases; the risk of bias was high for all studies. The results of the review pointed out that the implementation of AI in the child abuse and neglect field lagged compared to other medical fields. Furthermore, the evaluation of the risk of bias suggested that future studies should provide an appropriate choice of sample size, validation, and management of overfitting, optimism, and missing data.

A systematic review of ethical challenges and opportunities of addressing domestic violence with AI-technologies and online tools

Domestic violence remains a pressing complex social problem of people of any gender, age, socio-economic status, and ethno-cultural background, an issue that worsened worldwide during the COVID-19 pandemic. Digital, online, or artificial intelligence-based smart technological services, applications, and tools provide novel approaches in addressing domestic violence, including intimate partner violence. This systematic literature review analyses the ethical challenges and opportunities these (protective) digital and smart technologies provide to the stakeholders involved. Our results highlight that the public health and societal issue are the leading narratives of domestic violence, which is predominantly interpreted as gender-based violence. The review highlights an emerging trend of the role of machine learning- and artificial intelligence-based approaches in identifying and preventing domestic violence. However, we argue that little recommendation is available to professionals about how to use these approaches in a responsible way, and that the smartness of high-tech technologies is often challenged by basic-level technologies from perpetrators, creating an imbalance that also limits an impactful development of a comprehensive socio-technical regime that serves the safety and resilience of families in their communal setting.

An insight into diagnosis of depression using machine learning techniques: a systematic review

BACKGROUND

In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis.

METHODS

This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis.

RESULTS

A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test.

CONCLUSION

The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.