Method for monitoring psychological profiles of the legal and psychological effect on child victims of child abuse

Main Article Content

Diana Elizabeth Correa Manzano
Marcela Anarcaly Zambrano Olvera
Deinier Ros Álvarez

Abstract

Child sexual abuse, a complex issue that has impacted numerous children and young people throughout history, is hampered by its own complexity in its identification. Despite the high incidence of violence between minors, child sexual abuse often goes unnoticed. It requires special attention due to its difficulty in being detected, especially when it occurs within the family, involving figures such as biological parents, stepfathers, uncles or close friends. The present research proposes the development of a method for controlling psychological profiles of legal and psychological effects in minor victims of child abuse. The study focuses on the development of a neutrosophic method to evaluate and control the psychological profiles of victims of child abuse from a legal and psychological perspective. A tool is provided to identify and understand the impact of abuse on the emotional and mental well-being of children, as well as their ability to participate in legal processes. The proposed method seeks to improve early detection of the effects of abuse, facilitate timely intervention and provide more effective support to affected minors. The interdisciplinary approach of the study aims to integrate the understanding of legal and psychological aspects to comprehensively address the needs of victims of child abuse

Downloads

Download data is not yet available.

Article Details

How to Cite
Method for monitoring psychological profiles of the legal and psychological effect on child victims of child abuse. (2024). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 32, 37-46. https://fs.unm.edu/NCML2/index.php/112/article/view/518
Section
Articles

How to Cite

Method for monitoring psychological profiles of the legal and psychological effect on child victims of child abuse. (2024). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 32, 37-46. https://fs.unm.edu/NCML2/index.php/112/article/view/518