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A coding tool and abuse data for female asylum seekers
With 1 in 3 women affected, accounting for one billion women worldwide, Violence Against Women (VAW) constitutes one of the widest reaching human rights violations globally. Although the forms they take may vary, these abuses are not confined to a single social class, geographic region, or culture....
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7330134/ https://www.ncbi.nlm.nih.gov/pubmed/32637508 http://dx.doi.org/10.1016/j.dib.2020.105912 |
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author | Aguirre, Nicole G. Milewski, Andrew R. Shin, Joseph Ottenheimer, Deborah |
author_facet | Aguirre, Nicole G. Milewski, Andrew R. Shin, Joseph Ottenheimer, Deborah |
author_sort | Aguirre, Nicole G. |
collection | PubMed |
description | With 1 in 3 women affected, accounting for one billion women worldwide, Violence Against Women (VAW) constitutes one of the widest reaching human rights violations globally. Although the forms they take may vary, these abuses are not confined to a single social class, geographic region, or culture. Existing studies have yet to describe the full burden of abuse that asylum-seeking women endure throughout their lifetimes. We describe a novel coding tool that classifies types of abuse, identifies abuse perpetrators, and estimates how long and how often each abuse was experienced. The authors used this tool to describe and categorize the abuses endured by 85 cisgender, adult women seeking asylum in the United States who presented to the Weill Cornell Center for Human Rights for forensic medical evaluations from 2013 to 2017. We reviewed a total of 180 legal and forensic medical affidavits that were written in support of the applicants’ asylum claims. Using the coding tool, we identified each abuse, classified every perpetrator, and, whenever possible, estimated how long and how frequently each abuse was endured. Interpretations of the raw data contained in this article and a discussion of their significance can be found in our associated publication: “Gender-Based Violence experienced by Women Seeking Asylum in the United State: A Lifetime of Multiple Traumas Inflicted by Multiple Perpetrators” [1]. The coding instrument described herein characterizes VAW by classifying the narrative data that are included in interviews, focus groups, medical records, and the like. Our coding instrument is the first of its kind to describe all types and severities of violence endured by women, classify the perpetrators of that violence, and delineate the timeline of violence over each individual's life. We hope that this holistic approach to classifying and describing VAW will enable other research groups to examine untested or unrealized associations between victims, perpetrators, and abuses. Ultimately, obtaining more complete data will empower us to advocate more effectively and to design more comprehensive care for victims of VAW. |
format | Online Article Text |
id | pubmed-7330134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-73301342020-07-06 A coding tool and abuse data for female asylum seekers Aguirre, Nicole G. Milewski, Andrew R. Shin, Joseph Ottenheimer, Deborah Data Brief Medicine and Dentistry With 1 in 3 women affected, accounting for one billion women worldwide, Violence Against Women (VAW) constitutes one of the widest reaching human rights violations globally. Although the forms they take may vary, these abuses are not confined to a single social class, geographic region, or culture. Existing studies have yet to describe the full burden of abuse that asylum-seeking women endure throughout their lifetimes. We describe a novel coding tool that classifies types of abuse, identifies abuse perpetrators, and estimates how long and how often each abuse was experienced. The authors used this tool to describe and categorize the abuses endured by 85 cisgender, adult women seeking asylum in the United States who presented to the Weill Cornell Center for Human Rights for forensic medical evaluations from 2013 to 2017. We reviewed a total of 180 legal and forensic medical affidavits that were written in support of the applicants’ asylum claims. Using the coding tool, we identified each abuse, classified every perpetrator, and, whenever possible, estimated how long and how frequently each abuse was endured. Interpretations of the raw data contained in this article and a discussion of their significance can be found in our associated publication: “Gender-Based Violence experienced by Women Seeking Asylum in the United State: A Lifetime of Multiple Traumas Inflicted by Multiple Perpetrators” [1]. The coding instrument described herein characterizes VAW by classifying the narrative data that are included in interviews, focus groups, medical records, and the like. Our coding instrument is the first of its kind to describe all types and severities of violence endured by women, classify the perpetrators of that violence, and delineate the timeline of violence over each individual's life. We hope that this holistic approach to classifying and describing VAW will enable other research groups to examine untested or unrealized associations between victims, perpetrators, and abuses. Ultimately, obtaining more complete data will empower us to advocate more effectively and to design more comprehensive care for victims of VAW. Elsevier 2020-06-21 /pmc/articles/PMC7330134/ /pubmed/32637508 http://dx.doi.org/10.1016/j.dib.2020.105912 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Medicine and Dentistry Aguirre, Nicole G. Milewski, Andrew R. Shin, Joseph Ottenheimer, Deborah A coding tool and abuse data for female asylum seekers |
title | A coding tool and abuse data for female asylum seekers |
title_full | A coding tool and abuse data for female asylum seekers |
title_fullStr | A coding tool and abuse data for female asylum seekers |
title_full_unstemmed | A coding tool and abuse data for female asylum seekers |
title_short | A coding tool and abuse data for female asylum seekers |
title_sort | coding tool and abuse data for female asylum seekers |
topic | Medicine and Dentistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7330134/ https://www.ncbi.nlm.nih.gov/pubmed/32637508 http://dx.doi.org/10.1016/j.dib.2020.105912 |
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