A text-based approach to measuring opioid-related risk among families involved in the child welfare system.

BACKGROUND: The public health significance of the opioid epidemic is well-established. However, few states collect data on opioid problems among families involved in child welfare services. The absence of data creates significant barriers to understanding the impact of opioids on the service system and the needs of families being served. OBJECTIVE: This study sought to validate binary and count-based indicators of opioid-related maltreatment risk based on mentions of opioid use in written child welfare summaries. DATA AND PROCEDURES: We developed a comprehensive list of terms referring to opioid street drugs and pharmaceuticals. This terminology list was used to scan and flag investigator summaries from an extensive collection of investigations (N = 362,754) obtained from a state-based child welfare system in the United States. Associations between mentions of opioid use and investigators' decisions to substantiate maltreatment and remove a child from home were tested within a framework of a priori hypotheses. RESULTS: Approximately 6.3% of all investigations contained one or more opioid use mentions. Opioid mentions exhibited practically signficant associations with investigator decisions. One in ten summaries that were substantiated had an opioid mention. One in five investigations that led to the out-of-home placement of a child contained an opioid mention. CONCLUSION: This study demonstrates the feasibility of using simple text mining procedures to extract information from unstructured text documents. These methods provide novel opportunities to build insights into opioid-related problems among families involved in a child welfare system when structured data are not available.

"We Just Take Care of Each Other": Navigating 'Chosen Family' in the Context of Health, Illness, and the Mutual Provision of Care amongst Queer and Transgender Young Adults.

"Chosen family"-families formed outside of biological or legal (bio-legal) bonds-is a signature of the queer experience. Therefore, we address the stakes of "chosen family" for queer and transgender (Q/T) young adults in terms of health, illness and the mutual provision of care. "Chosen family" is a refuge specifically generated by and for the queer experience, so we draw upon anthropological theory to explore questions of queer kinship in terms of care. We employ a phenomenological approach to semi-structured interviews (n = 11), open coding, and thematic analysis of transcriptions to meet our aims: (1) Develop an understanding of the beliefs and values that form the definition of "chosen family" for Q/T young adults; and (2) Understand the ways in which "chosen family" functions in terms of care for health and illness. Several themes emerged, allowing us to better understand the experiences of this population in navigating the concept of "chosen family" within and beyond health care settings. Emergent themes include: (1) navigating medical systems; (2) leaning on each other; and (3) mutual aid. These findings are explored, as are the implications of findings for how health care professionals can better engage Q/T individuals and their support networks.

Prevalence and context of firearms-related problems in child protective service investigations.

BACKGROUND: Despite the significance of firearm safety, we need additional data to understand the prevalence and context surrounding firearm-related problems within the child welfare system. OBJECTIVE: Estimate proportion of cases reporting a firearm-related problem during case initiation and the contexts in which these problems exist. SAMPLE AND SETTING: 75,809 caseworker-written investigation summaries that represented all substantiated referrals of maltreatment in Michigan from 2015 to 2017. METHODS: We developed an expert dictionary of firearm-related terms to search investigation summaries. We retrieved summaries that contained any of the terms to confirm whether a firearm was present (construct accurate) and whether it posed a threat to the child. Finally, we coded summaries that contained firearm-related problems to identify contexts in which problems exist. RESULTS: Of the 75,809 substantiated cases, the dictionary flagged 2397 cases that used a firearm term (3.2 %), with a construct accuracy rate of 96 %. Among construct accurate cases, 79 % contained a firearm-related problem. The most common intent for a firearm-related problem was violence against a person (45 %). The co-occurrence of domestic violence and/or substance use with a firearm-related problem was high (41 % and 48 %, respectively). 49 % of summaries that contained a firearm-related problem did not provide information regarding storage. CONCLUSION: When caseworkers document a firearm within investigative summaries, a firearm-related risk to the child likely exists. Improved documentation of firearms and storage practices among investigated families may better identify families needing firearm-related services.

Detecting substance-related problems in narrative investigation summaries of child abuse and neglect using text mining and machine learning.

BACKGROUND: State child welfare agencies collect, store, and manage vast amounts of data. However, they often do not have the right data, or the data is problematic or difficult to inform strategies to improve services and system processes. Considerable resources are required to read and code these text data. Data science and text mining offer potentially efficient and cost-effective strategies for maximizing the value of these data. OBJECTIVE: The current study tests the feasibility of using text mining for extracting information from unstructured text to better understand substance-related problems among families investigated for abuse or neglect. METHOD: A state child welfare agency provided written summaries from investigations of child abuse and neglect. Expert human reviewers coded 2956 investigation summaries based on whether the caseworker observed a substance-related problem. These coded documents were used to develop, train, and validate computer models that could perform the coding on an automated basis. RESULTS: A set of computer models achieved greater than 90% accuracy when judged against expert human reviewers. Fleiss kappa estimates among computer models and expert human reviewers exceeded .80, indicating that expert human reviewer ratings are exchangeable with the computer models. CONCLUSION: These results provide compelling evidence that text mining procedures can be a cost-effective and efficient solution for extracting meaningful insights from unstructured text data. Additional research is necessary to understand how to extract the actionable insights from these under-utilized stores of data in child welfare.