Black and Latinx Primary Caregiver Considerations for Developing and Implementing a Machine Learning-Based Model for Detecting Child Abuse and Neglect With Implications for Racial Bias Reduction: Qualitative Interview Study With Primary Caregivers.

Background: Child abuse and neglect, once viewed as a social problem, is now an epidemic. Moreover, health providers agree that existing stereotypes may link racial and social class issues to child abuse. The broad adoption of electronic health records (EHRs) in clinical settings offers a new avenue for addressing this epidemic.

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 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.

Developing machine learning-based models to help identify child abuse and neglect: key ethical challenges and recommended solutions.

Child abuse and neglect are public health issues impacting communities throughout the United States. The broad adoption of electronic health records (EHR) in health care supports the development of machine learning-based models to help identify child abuse and neglect. Employing EHR data for child abuse and neglect detection raises several critical ethical considerations.

Considerations for development of child abuse and neglect phenotype with implications for reduction of racial bias: a qualitative study.

Objective: The study provides considerations for generating a phenotype of child abuse and neglect in Emergency Departments (ED) using secondary data from electronic health records (EHR). Implications will be provided for racial bias reduction and the development of further decision support tools to assist in identifying child abuse and neglect.

Materials And Methods: We conducted a qualitative study using in-depth interviews with 20 pediatric clinicians working in a single pediatric ED to gain insights about generating an EHR-based phenotype to identify children at risk for abuse and neglect.

Addressing the inappropriate use of force by police in the United States and beyond: A behavioral and social science perspective.

Recent high-profile incidents involving the deadly application of force in the United States sparked worldwide protests and renewed scrutiny of police practices as well as scrutiny of relations between police officers and minoritized communities. In this report, we consider the inappropriate use of force by police from the perspective of behavioral and social science inquiry related to aggression, violence, and intergroup relations. We examine the inappropriate use of force by police in the context of research on modern policing as well as critical race theory and offer five recommendations suggested by contemporary theory and research.

Avoiding fights on social media: Strategies youth leverage to navigate conflict in a digital era.

Aims: Emerging qualitative work documents that social media conflict sometimes results in violence in impoverished urban neighborhoods. Not all experiences of social media conflict lead to violence, however, and youth ostensibly use a variety of techniques to avoid violent outcomes. Little research has explored the daily violence prevention strategies youth use on social media, an important gap given the omnipresence of social media in youth culture.

Artificial Intelligence and Inclusion: Formerly Gang-Involved Youth as Domain Experts for Analyzing Unstructured Twitter Data.

Mining social media data for studying the human condition has created new and unique challenges. When analyzing social media data from marginalized communities, algorithms lack the ability to accurately interpret off-line context, which may lead to dangerous assumptions about and implications for marginalized communities. To combat this challenge, we hired formerly gang-involved young people as domain experts for contextualizing social media data in order to create inclusive, community-informed algorithms.

Contextual Analysis of Social Media: The Promise and Challenge of Eliciting Context in Social Media Posts with Natural Language Processing.

While natural language processing affords researchers an opportunity to automatically scan millions of social media posts, there is growing concern that automated computational tools lack the ability to understand context and nuance in human communication and language. This article introduces a critical systematic approach for extracting culture, context and nuance in social media data. The Contextual Analysis of Social Media (CASM) approach considers and critiques the gap between inadequacies in natural language processing tools and differences in geographic, cultural, and age-related variance of social media use and communication.

Accommodating Grief on Twitter: An Analysis of Expressions of Grief Among Gang Involved Youth on Twitter Using Qualitative Analysis and Natural Language Processing.

There is a dearth of research investigating youths' experience of grief and mourning after the death of close friends or family. Even less research has explored the question of how youth use social media sites to engage in the grieving process. This study employs qualitative analysis and natural language processing to examine tweets that follow 2 deaths.

Expressions of loss predict aggressive comments on Twitter among gang-involved youth in Chicago.

Recent studies suggest social media shapes the transmission of firearm violence in high-poverty, urban neighborhoods. However, the exact pathways by which content on social media becomes threatening has not been studied. We consider a dataset of tweets by gang-involved Chicago youth that are coded for expressions of aggression and/or loss.

Tweets, Gangs, and Guns: A Snapshot of Gang Communications in Detroit.

The aim of this study is to determine the frequency of violent and criminal Twitter communications among gang-affiliated individuals in Detroit, Michigan. We analyzed 8.5 million Detroit gang members' tweets from January 2013 to March 2014 to assess whether they contained Internet banging-related keywords.

Post-Discharge Needs of Victims of Gun Violence in Chicago: A Qualitative Study.

The purpose of this study was to understand the post-discharge needs of violently injured patients and their families to improve health outcomes and reduce the impact of gun violence. We recruited 10 patients from the trauma registry of a Midwestern university hospital with a Level 1 Trauma Center (L1TC). After obtaining the informed consent, semi-structured, face-to-face, in-depth interviews were conducted.

Hospital-based violence intervention programs targeting adult populations: an Eastern Association for the Surgery of Trauma evidence-based review.

Background: Violent injury and reinjury take a devastating toll on distressed communities. Many trauma centers have created hospital-based violent injury prevention programs (HVIP) to address psychosocial, educational, and mental health needs of injured patients that may contribute to reinjury.

Objectives: To evaluate the overall effectiveness of HVIPs for violent injury prevention.

Understanding the Correlates of Face-to-Face and Cyberbullying Victimization Among U.S. Adolescents: A Social-Ecological Analysis.

Using a national sample of 7,533 U.S. adolescents in grades 6-10, this study compares the social-ecological correlates of face-to-face and cyberbullying victimization.

A Systematic Review of Research Strategies Used in Qualitative Studies on School Bullying and Victimization.

School bullying and victimization are serious social problems in schools. Most empirical studies on bullying and peer victimization are quantitative and examine the prevalence of bullying, associated risk and protective factors, and negative outcomes. Conversely, there is limited qualitative research on the experiences of children and adolescents related to school bullying and victimization.