Neutrosophic Cognitive Map for the evaluation of the adequate protection of adolescents against drug use

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Segundo Heriberto Granja Huacón
José Alexander Morales Alvear
Santiago Alexander Olvera Manobanda
Alexander Raúl Morejón Vásquez

Abstract

The research focuses on drug use among adolescents aged 12 to 17 years in the Ciudadela Barrio Lindo during the year 2023. The main objective is to determine the magnitude of the problem and its impact on the safety and well-being of adolescents, based on the implementation of a Neutrosophic Cognitive Map to assess the proper protection of young people from the consumption of these substances. This phenomenon is presented as a complex challenge, influenced by socioeconomic and cultural factors, where the lack of opportunities and social pressure are significant determinants. Adolescents, finding themselves in an environment characterized by deficiencies in employment and educational prospects and the need to be accepted in social groups, become more vulnerable to drug use. To effectively address this problem, a comprehensive approach is proposed that includes prevention policies, educational programs, and a strong family support system. It is essential to implement awareness and prevention strategies in schools, strengthen community networks and offer attractive economic alternatives for young people. Likewise, offering tools and resources to families will strengthen their capacity to provide support to their children. Only through this multifaceted approach will it be possible to mitigate the impact of drug use and promote a safer and healthier environment for adolescents in Ciudadela Barrio Lindo.

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How to Cite
Neutrosophic Cognitive Map for the evaluation of the adequate protection of adolescents against drug use. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 37, 21-29. https://fs.unm.edu/NCML2/index.php/112/article/view/696
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How to Cite

Neutrosophic Cognitive Map for the evaluation of the adequate protection of adolescents against drug use. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 37, 21-29. https://fs.unm.edu/NCML2/index.php/112/article/view/696