Predictors of General Deviance in the Context of COVID-19.

Int J Offender Ther Comp Criminol

El Chuco Winner's Circle, El Paso, TX, USA.

Published: July 2023

This study examined predictors of individual general deviance (i.e., substance use, risk-taking, property crime, and interpersonal conflict/violence) within the context of COVID-19, focusing on the role of prior deviance, opportunities for crime, and levels of COVID-19- related stress. Our study showed that while some predictors relating to opportunity and strain were predictive of general deviance during the pandemic, few maintained statistical significance once controls for deviant behavior before the pandemic were included in the analyses, indicating the importance of within-individual behavioral stability over time. Further, respondents who participated in deviance prior to the pandemic were more likely to engage in other forms of criminal and high-risk activities during the pandemic. The close connections between criminal and high-risk behavior may imply that even if overall crime rates decreased during the pandemic, within-person behavioral patterns remained stable.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10315875PMC
http://dx.doi.org/10.1177/0306624X231172644DOI Listing

Publication Analysis

Top Keywords

general deviance
12
context covid-19
8
criminal high-risk
8
deviance
5
pandemic
5
predictors general
4
deviance context
4
covid-19 study
4
study examined
4
examined predictors
4

Similar Publications

Background: Cluster randomized trials, which often enroll a small number of clusters, can benefit from constrained randomization, selecting a final randomization scheme from a set of known, balanced randomizations. Previous literature has addressed the suitability of adjusting the analysis for the covariates that were balanced in the design phase when the outcome is continuous or binary. Here we extended this work to time-to-event outcomes by comparing two model-based tests and a newly derived permutation test.

View Article and Find Full Text PDF

Predicting changes in agricultural yields under climate change scenarios and their implications for global food security.

Sci Rep

January 2025

School of BioSciences, Centre of Excellence for Biosecurity Risk Analysis, University of Melbourne, Melbourne, 3010, Australia.

Climate change has direct impacts on current and future agricultural productivity. Statistical meta-analysis models can be used to generate expectations of crop yield responses to climatic factors by pooling data from controlled experiments. However, methodological challenges in performing these meta-analyses, together with combined uncertainty from various sources, make it difficult to validate model results.

View Article and Find Full Text PDF

Background: The cotton jassid, Amrasca biguttula, a dangerous and polyphagous pest, has recently invaded the Middle East, Africa and South America, raising concerns about the future of cotton and other food crops including okra, eggplant and potato. However, its potential distribution remains largely unknown, posing a challenge in developing effective phytosanitary strategies. We used an ensemble model of six machine-learning algorithms including random forest, maxent, support vector machines, classification and regression tree, generalized linear model and boosted regression trees to forecast the potential distribution of A.

View Article and Find Full Text PDF

This study aimed to evaluate and compare Bayesian predictive models to identify and quantify the key household inputs affecting cattle milk production in Tanzania. A sample of 1,266 households with at least one milked cow was extracted from the National Panel Survey datasets, the data were collected in 2012/2013 (wave 3), 2014/2015 (wave 4), and 2020/2021 (wave 5). Two generalized linear and generalized additive mixed models were fitted using the Integrated Nested Laplace Approximation.

View Article and Find Full Text PDF

This study investigates factors influencing physical activity based on the Transtheoretical model (TTM) among adolescents. This study was conducted on 745 individuals between the ages of 12 and 16 years and was analyzed using a generalized linear model (GLM) approach with appropriate link functions using both classical and Bayesian frameworks. The results show that in model 1, the probit link function is a more appropriate approach to determine the risk factors for physical activity.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!