Evidence map›Paper›PMID 33518825›Full record

ArticleJournal of criminal justice

When COVID-19 and guns meet: A rise in shootings.

Dae-Young Kim, Scott W Phillips

Open access · greenAbstract read
In one paragraph

Article in Journal of criminal justice. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 1 pooled it
12.1field-weighted citation impact, top 1% of its field
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

36 citing papers in PubMed, 1 synthesis or guideline pooled it, 108 citations in OpenAlex.

  1. Pooled it
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  9. Predicting Short Time-to-Crime Guns: a Machine Learning Analysis of California Transaction Records (2010-2021).Journal of urban health : bulletin of the New York Academy of Medicine · 2024
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  13. Trends and Sources of Crime Guns in California: 2010-2021.Journal of urban health : bulletin of the New York Academy of Medicine · 2023
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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

2 authors at 1 institution in 1 country.

Dae-Young KimState University of New York - Buffalo State, United States.
Scott W PhillipsState University of New York - Buffalo State, United States.
University at Buffalo, State University of New York · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe present study examines the impact of the COVID-19 stay-at-home order on gun violence in Buffalo, New York: fatal shootings, all non-fatal shootings, non-fatal shootings with injury, and non-fatal shootings without injury. It also estimated its impact on gang and non-gang related shootings.

methodsWeekly crime data are analyzed at the city level using ARIMA and poisson models. Forecasting is used to verify the validity of both ARIMA and poisson models.

resultsThe effect of the pandemic was conditional upon the types of gun violence and impact models of intervention. The pandemic caused a temporary increase in fatal shootings while leading to a long-term increase in all non-fatal shootings, non-fatal shootings with injury, non-fatal shootings without injury, and gang related shootings.

conclusionsThe pandemic has changed the volume of gun violence possibly due to increased strain and/or changed routine activities. This study not only promotes further research but also has policy implications for public health and safety. From a public policy perspective, criminal justice agencies should focus more attention and resources on gun violence resulting from a sense of strain and fear among individuals during the pandemic.

Indexed as

COVID-19Fatal shootingsGang related shootingsGun violenceNon-fatal shootingsPandemicStay-at-home orders

Identifiers

PMID33518825
PMCPMC7825997
OpenAlexW3123957893

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.