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Uncovering social-contextual and individual mental health factors associated with violence via computational inference
The identification of human violence determinants has sparked multiple questions from different academic fields. Innovative methodological assessments of the weight and interaction of multiple determinants are still required. Here, we examine multiple features potentially associated with confessed a...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892360/ https://www.ncbi.nlm.nih.gov/pubmed/33659906 http://dx.doi.org/10.1016/j.patter.2020.100176 |
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author | Santamaría-García, Hernando Baez, Sandra Aponte-Canencio, Diego Mauricio Pasciarello, Guido Orlando Donnelly-Kehoe, Patricio Andrés Maggiotti, Gabriel Matallana, Diana Hesse, Eugenia Neely, Alejandra Zapata, José Gabriel Chiong, Winston Levy, Jonathan Decety, Jean Ibáñez, Agustín |
author_facet | Santamaría-García, Hernando Baez, Sandra Aponte-Canencio, Diego Mauricio Pasciarello, Guido Orlando Donnelly-Kehoe, Patricio Andrés Maggiotti, Gabriel Matallana, Diana Hesse, Eugenia Neely, Alejandra Zapata, José Gabriel Chiong, Winston Levy, Jonathan Decety, Jean Ibáñez, Agustín |
author_sort | Santamaría-García, Hernando |
collection | PubMed |
description | The identification of human violence determinants has sparked multiple questions from different academic fields. Innovative methodological assessments of the weight and interaction of multiple determinants are still required. Here, we examine multiple features potentially associated with confessed acts of violence in ex-members of illegal armed groups in Colombia (N = 26,349) through deep learning and feature-derived machine learning. We assessed 162 social-contextual and individual mental health potential predictors of historical data regarding consequentialist, appetitive, retaliative, and reactive domains of violence. Deep learning yields high accuracy using the full set of determinants. Progressive feature elimination revealed that contextual factors were more important than individual factors. Combined social network adversities, membership identification, and normalization of violence were among the more accurate social-contextual factors. To a lesser extent the best individual factors were personality traits (borderline, paranoid, and antisocial) and psychiatric symptoms. The results provide a population-based computational classification regarding historical assessments of violence in vulnerable populations. |
format | Online Article Text |
id | pubmed-7892360 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-78923602021-03-02 Uncovering social-contextual and individual mental health factors associated with violence via computational inference Santamaría-García, Hernando Baez, Sandra Aponte-Canencio, Diego Mauricio Pasciarello, Guido Orlando Donnelly-Kehoe, Patricio Andrés Maggiotti, Gabriel Matallana, Diana Hesse, Eugenia Neely, Alejandra Zapata, José Gabriel Chiong, Winston Levy, Jonathan Decety, Jean Ibáñez, Agustín Patterns (N Y) Article The identification of human violence determinants has sparked multiple questions from different academic fields. Innovative methodological assessments of the weight and interaction of multiple determinants are still required. Here, we examine multiple features potentially associated with confessed acts of violence in ex-members of illegal armed groups in Colombia (N = 26,349) through deep learning and feature-derived machine learning. We assessed 162 social-contextual and individual mental health potential predictors of historical data regarding consequentialist, appetitive, retaliative, and reactive domains of violence. Deep learning yields high accuracy using the full set of determinants. Progressive feature elimination revealed that contextual factors were more important than individual factors. Combined social network adversities, membership identification, and normalization of violence were among the more accurate social-contextual factors. To a lesser extent the best individual factors were personality traits (borderline, paranoid, and antisocial) and psychiatric symptoms. The results provide a population-based computational classification regarding historical assessments of violence in vulnerable populations. Elsevier 2021-02-12 /pmc/articles/PMC7892360/ /pubmed/33659906 http://dx.doi.org/10.1016/j.patter.2020.100176 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Santamaría-García, Hernando Baez, Sandra Aponte-Canencio, Diego Mauricio Pasciarello, Guido Orlando Donnelly-Kehoe, Patricio Andrés Maggiotti, Gabriel Matallana, Diana Hesse, Eugenia Neely, Alejandra Zapata, José Gabriel Chiong, Winston Levy, Jonathan Decety, Jean Ibáñez, Agustín Uncovering social-contextual and individual mental health factors associated with violence via computational inference |
title | Uncovering social-contextual and individual mental health factors associated with violence via computational inference |
title_full | Uncovering social-contextual and individual mental health factors associated with violence via computational inference |
title_fullStr | Uncovering social-contextual and individual mental health factors associated with violence via computational inference |
title_full_unstemmed | Uncovering social-contextual and individual mental health factors associated with violence via computational inference |
title_short | Uncovering social-contextual and individual mental health factors associated with violence via computational inference |
title_sort | uncovering social-contextual and individual mental health factors associated with violence via computational inference |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892360/ https://www.ncbi.nlm.nih.gov/pubmed/33659906 http://dx.doi.org/10.1016/j.patter.2020.100176 |
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