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. 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.

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. Our recommendations are aimed at policymakers, law enforcement administrators, and scholars and are as follows: (1) Implement public policies that can reduce inappropriate use of force directly and through the reduction of broader burdens on the routine activities of police officers. (2) For officers frequently engaged in use-of-force incidents, ensure that best practice, evidence-based treatments are available and required. (3) Improve and increase the quality and delivery of noncoercive conflict resolution training for all officers, along with police administrative policies and supervision that support alternatives to the use of force, both while scaling back the militarization of police departments. (4) Continue the development and evaluation of multicomponent interventions for police departments, but ensure they incorporate evidence-based, field-tested components. (5) Expand research in the behavioral and social sciences aimed at understanding and managing use-of-force by police and reducing its disproportionate impact on minoritized communities, and expand funding for these lines of inquiry.

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. This paper examines youth strategies and factors that avoid violence resulting from social media conflict. METHOD: Four focus groups with 41 teenagers of color solicited strategies to prevent violence resulting from social media conflict. Three coders analyzed data in Dedoose, guided by systematic textual coding using a multi-step thematic analysis. RESULTS: Four approaches emerged to avoiding violence from social media conflict: avoid, de-escalate, reach out for help, and bystander intervention. CONCLUSION: Our findings position youth as key players in efforts to prevent violence from resulting from social media conflict.

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. Utilizing data from the Gang Intervention and Computer Science Project-a comprehensive analysis of Twitter data from gang-involved youth in Chicago-we describe the process of involving formerly gang-involved young people in developing a new part-of-speech tagger and content classifier for a prototype natural language processing system that detects aggression and loss in Twitter data. We argue that involving young people as domain experts leads to more robust understandings of context, including localized language, culture, and events. These insights could change how data scientists approach the development of corpora and algorithms that affect people in marginalized communities and who to involve in that process. We offer a contextually driven interdisciplinary approach between social work and data science that integrates domain insights into the training of qualitative annotators and the production of algorithms for positive social impact.

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. We found that 4.7% of gang-affiliated user tweets consisted of terms related to violence and crime. Violence and crime-related communications fell into 4 main categories: (a) beefing (267,221 tweets), (b) grief (79,971 tweets), (c) guns (3,551 tweets), and (d) substance use and distribution (47,638 tweets). Patterns in violent and criminal communication that may be helpful in predicting future gang activities were identified, which has implications for violence prevention research, practice, and policy.

Harnessing the Power of Machine Learning and Electronic Health Records to Support Child Abuse and Neglect Identification in Emergency Department Settings.

Emergency departments (EDs) are pivotal in detecting child abuse and neglect, but this task is often complex. Our study developed a machine learning model using structured and unstructured electronic health record (EHR) data to predict when children in EDs might need intervention from child protective services. We used a case-control study design, analyzing data from a pediatric ED. Clinical notes were processed with natural language processing (NLP) techniques to identify suspected cases and matched in a 1:9 ratio to ensure dataset balance. The features from these notes were combined with structured EHR data to construct a model using the XGBoost algorithm. The model achieved a precision of 0.95, recall of 0.88, and F1-score of 0.92, with improvements seen from integrating NLP-derived data. Key indicators for abuse included hospital admissions, extended ED stays, and specific clinical orders. The model's accuracy and the utility of NLP suggest the potential for EDs to better identify at-risk children. Future work should validate the model further and explore additional features while considering ethical implications to aid healthcare providers in safeguarding children.

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. To reduce racial bias and improve the development, implementation, and outcomes of machine learning (ML)-based models that use EHR data, it is crucial to involve marginalized members of the community in the process. OBJECTIVE: This study elicited Black and Latinx primary caregivers' viewpoints regarding child abuse and neglect while living in underserved communities to highlight considerations for designing an ML-based model for detecting child abuse and neglect in emergency departments (EDs) with implications for racial bias reduction and future interventions. METHODS: We conducted a qualitative study using in-depth interviews with 20 Black and Latinx primary caregivers whose children were cared for at a single pediatric tertiary-care ED to gain insights about child abuse and neglect and their experiences with health providers. RESULTS: Three central themes were developed in the coding process: (1) primary caregivers' perspectives on the definition of child abuse and neglect, (2) primary caregivers' experiences with health providers and medical documentation, and (3) primary caregivers' perceptions of child protective services. CONCLUSIONS: Our findings highlight essential considerations from primary caregivers for developing an ML-based model for detecting child abuse and neglect in ED settings. This includes how to define child abuse and neglect from a primary caregiver lens. Miscommunication between patients and health providers can potentially lead to a misdiagnosis, and therefore, have a negative impact on medical documentation. Additionally, the outcome and application of the ML-based models for detecting abuse and neglect may cause additional harm than expected to the community. Further research is needed to validate these findings and integrate them into creating an ML-based model.

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. RESULTS: Three central themes emerged from the interviews: (1) Challenges in diagnosing child abuse and neglect, (2) Health Discipline Differences in Documentation Styles in EHR, and (3) Identification of potential racial bias through documentation. DISCUSSION: Our findings highlight important considerations for generating a phenotype for child abuse and neglect using EHR data. First, information-related challenges include lack of proper previous visit history due to limited information exchanges and scattered documentation within EHRs. Second, there are differences in documentation styles by health disciplines, and clinicians tend to document abuse in different document types within EHRs. Finally, documentation can help identify potential racial bias in suspicion of child abuse and neglect by revealing potential discrepancies in quality of care, and in the language used to document abuse and neglect. CONCLUSIONS: Our findings highlight challenges in building an EHR-based risk phenotype for child abuse and neglect. Further research is needed to validate these findings and integrate them into creation of an EHR-based risk phenotype.

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. This article applied a phenomenological approach to discuss and provide recommendations for key ethical issues related to machine learning-based risk models development and evaluation: (1) biases in the data; (2) clinical documentation system design issues; (3) lack of centralized evidence base for child abuse and neglect; (4) lack of "gold standard "in assessment and diagnosis of child abuse and neglect; (5) challenges in evaluation of risk prediction performance; (6) challenges in testing predictive models in practice; and (7) challenges in presentation of machine learning-based prediction to clinicians and patients. We provide recommended solutions to each of the 7 ethical challenges and identify several areas for further policy and research.

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. CASM utilizes a team-based approach to analysis of social media data, explicitly informed by community expertise. We use of CASM to analyze Twitter posts from gang-involved youth in Chicago. We designed a set of experiments to evaluate the performance of a support vector machine using CASM hand-labeled posts against a distant model. We found that the CASM-informed hand-labeled data outperforms the baseline distant labels, indicating that the CASM labels capture additional dimensions of information that content-only methods lack. We then question whether this is helpful or harmful for gun violence prevention.

Analysis of Social Media Involvement in Violent Injury.

This cross-sectional study uses police agency­collected information to quantify the association among social media involvement, crime, and violence.

Posttraumatic stress symptoms in context: Examining trauma responses to violent exposures and homicide death among Black males in urban neighborhoods.

Concentrated disadvantage in urban communities places young Black men at disproportionate risk for exposure to violence and trauma. Homicide, a health disparity, positions Black males vulnerable to premature violent death and traumatic loss, particularly when peers are murdered. Posttraumatic stress disorder (PTSD) has been demonstrated as a health consequence for middle-income and White homicide survivors; however, understandings of traumatic stress among young Black men situated in contexts of chronic violence exposure remains limited. Guided by phenomenological variant of ecological systems theory (PVEST), the current study used in-depth qualitative interviews (average length: 90 min) to examine the presence and expression of traumatic stress symptoms among 37 young Black men (18-24) in Baltimore who experienced the homicide death of a loved one. Participants were recruited over 18 months through fieldwork at a large organization that serves Baltimore youth and young adults. Confidential participant interviews were audio recorded, transcribed verbatim, coded, and analyzed in ATLAS.ti. Pseudonyms were assigned to all participants. More than 70% of participants reported experiencing 2 or more Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-V)-defined posttraumatic stress symptoms. Hypervigilance was most frequently experienced and expressed as being on point. Findings identify the prevalence of traumatic stress symptoms among young Black men in urban contexts; identify contextually specific expressions of traumatic stress; and, present implications for the mental health and clinical treatment of Black males living in environments where no "post" exists. (PsycINFO Database Record