Using natural language processing approaches to characterize professional experiences of child welfare workers

Abstract

High turnover rates among child welfare workers, including caseworkers and supervisors, are associated with negative attitudes and perceptions about work (NAPW). Our study aimed to (a) explore how child welfare workers’ demographic characteristics, professional attributes, and their work narratives are related to NAPW and (b) extract themes from these narratives to understand their professional needs and concerns. In 2022, a statewide online survey was administered to active child welfare caseworkers and supervisors in a large Midwestern state. This survey, combining standardized scales and an open-ended question about work experiences, yielded numeric (N = 551 survey respondents) and text data (N = 2,193 words). We analyzed the numeric data using general linear modeling and the text data using natural language processing (NLP) techniques. Results showed that workers associated with higher NAPW were female, younger, from racial and ethnic minority backgrounds, less tenured, and more likely to be employed in public agencies and to work longer hours. They are predominantly caseworkers rather than supervisors. Worker role and agency affiliation had the largest effect sizes. NLP analysis showed that workers with more NAPW frequently used third-person pronouns and emotionally negative language, suggesting a disconnection from colleagues and job dissatisfaction. Caseworkers often used first-person singular pronouns, indicating feelings of isolation. Two key themes emerged: a) the need for better agency support during home visits and b) concerns over traumatic experiences and ineffective trauma-informed practices. The findings suggest that future interventions should prioritize the well-being of caseworkers in public agencies and consider language patterns in workers’ narratives to understand their vulnerability to NAPW.