Evidence map›Paper›PMID 36162487›Full record

ArticlePreventive medicine2022

Community implications for gun violence prevention during co-occurring pandemics; a qualitative and computational analysis study.

Desmond U Patton, Nathan Aguilar, Aviv Y Landau, Chris Thomas, Rachel Kagan, Tianai Ren, Eric Stoneberg, Timothy Wang, Daniel Halmos, Anish Saha and 2 more

Abstract read
In one paragraph

Article in Preventive medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Desmond U PattonSchool of Social Policy & Practice, Annenberg School for Communication, Department of Child and Adolescent Psychiatry and Behavioral Sciences, University of Pennsylvania, Philadelphia, USA. Electronic address: dp2787@columbia.edu.
Nathan AguilarColumbia School of Social Work, Columbia University, NYC, USA.
Aviv Y LandauSchool of Social Policy & Practice, University of Pennsylvania, Philadelphia, USA.
Chris ThomasColumbia Department of Electrical Engineering, Columbia University, NYC, USA.
Rachel KaganColumbia School of Social Work, Columbia University, NYC, USA.
Tianai RenColumbia School of Social Work, Columbia University, NYC, USA.
Eric StonebergColumbia School of Social Work, Columbia University, NYC, USA.
Timothy WangColumbia Department of Computer Science, Columbia University, NYC, USA.
Daniel HalmosColumbia Department of Computer Science, Columbia University, NYC, USA.
Anish SahaColumbia Department of Computer Science, Columbia University, NYC, USA.
Amith AnanthramColumbia Department of Computer Science, Columbia University, NYC, USA.
Kathleen McKeownColumbia Department of Computer Science, Columbia University, NYC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

COVID-19Gun ViolenceHumansNew York CityPandemicsViolenceBlack lives matterCOVID-19Defund the policeGun violenceQualitative and computational analysisSocial media

Identifiers

PMID36162487
PMCPMC9507780

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.