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Community implications for gun violence prevention during co-occurring pandemics; a qualitative and computational analysis study
This study provides insight into New York City residents' perceptions about violence after the outbreak of Coronavirus disease (COVID-19) based on information from communities in New York City Housing Authority (NYCHA) buildings. In this novel analysis, we used focus group and social media data...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9507780/ https://www.ncbi.nlm.nih.gov/pubmed/36162487 http://dx.doi.org/10.1016/j.ypmed.2022.107263 |
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author | Patton, Desmond U. Aguilar, Nathan Landau, Aviv Y. Thomas, Chris Kagan, Rachel Ren, Tianai Stoneberg, Eric Wang, Timothy Halmos, Daniel Saha, Anish Ananthram, Amith McKeown, Kathleen |
author_facet | Patton, Desmond U. Aguilar, Nathan Landau, Aviv Y. Thomas, Chris Kagan, Rachel Ren, Tianai Stoneberg, Eric Wang, Timothy Halmos, Daniel Saha, Anish Ananthram, Amith McKeown, Kathleen |
author_sort | Patton, Desmond U. |
collection | PubMed |
description | This study provides insight into New York City residents' perceptions about violence after the outbreak of Coronavirus disease (COVID-19) based on information from communities in New York City Housing Authority (NYCHA) buildings. In this novel analysis, we used focus group and social media data to confirm or reject findings from qualitative interviews. We first used data from 69 in-depth, semi-structured interviews with low-income residents and community stakeholders to further explore how violence impacts New York City's low-income residents of color, as well as the role of city government in providing tangible support for violence prevention during co-occurring health (COVID-19) and social (anti-Black racism) pandemics. Residents described how COVID-19 and the Black Lives Matter movement impacted safety in their communities while offering direct recommendations to improve safety. Residents also shared recommendations that indirectly improve community safety by addressing long term systemic issues. As the recruitment of interviewees was concluding, researchers facilitated two focus groups with 38 interviewees to discuss similar topics. In order to assess the degree to which the themes discovered in our qualitative interviews were shared by the broader community, we developed an integrative community data science study which leveraged natural language processing and computer vision techniques to study text and images on public social media data of 12 million tweets generated by residents. We joined computational methods with qualitative analysis through a social work lens and design justice principles to most accurately and holistically analyze the community perceptions of gun violence issues and potential prevention strategies. Findings indicate valuable community-based insights that elucidate how the co-occurring pandemics impact residents' experiences of gun violence and provide important implications for gun violence prevention in a digital era. |
format | Online Article Text |
id | pubmed-9507780 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Authors. Published by Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95077802022-09-26 Community implications for gun violence prevention during co-occurring pandemics; a qualitative and computational analysis study Patton, Desmond U. Aguilar, Nathan Landau, Aviv Y. Thomas, Chris Kagan, Rachel Ren, Tianai Stoneberg, Eric Wang, Timothy Halmos, Daniel Saha, Anish Ananthram, Amith McKeown, Kathleen Prev Med Article This study provides insight into New York City residents' perceptions about violence after the outbreak of Coronavirus disease (COVID-19) based on information from communities in New York City Housing Authority (NYCHA) buildings. In this novel analysis, we used focus group and social media data to confirm or reject findings from qualitative interviews. We first used data from 69 in-depth, semi-structured interviews with low-income residents and community stakeholders to further explore how violence impacts New York City's low-income residents of color, as well as the role of city government in providing tangible support for violence prevention during co-occurring health (COVID-19) and social (anti-Black racism) pandemics. Residents described how COVID-19 and the Black Lives Matter movement impacted safety in their communities while offering direct recommendations to improve safety. Residents also shared recommendations that indirectly improve community safety by addressing long term systemic issues. As the recruitment of interviewees was concluding, researchers facilitated two focus groups with 38 interviewees to discuss similar topics. In order to assess the degree to which the themes discovered in our qualitative interviews were shared by the broader community, we developed an integrative community data science study which leveraged natural language processing and computer vision techniques to study text and images on public social media data of 12 million tweets generated by residents. We joined computational methods with qualitative analysis through a social work lens and design justice principles to most accurately and holistically analyze the community perceptions of gun violence issues and potential prevention strategies. Findings indicate valuable community-based insights that elucidate how the co-occurring pandemics impact residents' experiences of gun violence and provide important implications for gun violence prevention in a digital era. The Authors. Published by Elsevier Inc. 2022-12 2022-09-24 /pmc/articles/PMC9507780/ /pubmed/36162487 http://dx.doi.org/10.1016/j.ypmed.2022.107263 Text en © 2022 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Patton, Desmond U. Aguilar, Nathan Landau, Aviv Y. Thomas, Chris Kagan, Rachel Ren, Tianai Stoneberg, Eric Wang, Timothy Halmos, Daniel Saha, Anish Ananthram, Amith McKeown, Kathleen Community implications for gun violence prevention during co-occurring pandemics; a qualitative and computational analysis study |
title | Community implications for gun violence prevention during co-occurring pandemics; a qualitative and computational analysis study |
title_full | Community implications for gun violence prevention during co-occurring pandemics; a qualitative and computational analysis study |
title_fullStr | Community implications for gun violence prevention during co-occurring pandemics; a qualitative and computational analysis study |
title_full_unstemmed | Community implications for gun violence prevention during co-occurring pandemics; a qualitative and computational analysis study |
title_short | Community implications for gun violence prevention during co-occurring pandemics; a qualitative and computational analysis study |
title_sort | community implications for gun violence prevention during co-occurring pandemics; a qualitative and computational analysis study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9507780/ https://www.ncbi.nlm.nih.gov/pubmed/36162487 http://dx.doi.org/10.1016/j.ypmed.2022.107263 |
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