Patterns of mental health services access among children involved in multiple systems

• In Event: Poster Session 09
  In Poster Session: PS 09 Topic - Prevention and Interventions

Abstract

Many mental health (MH) conditions emerge in childhood and adolescence, and without proper treatment or support, may threaten healthy development (Costello, 2016; Deighton et al., 2018). MH services provided by publicly-funded systems (e.g., education, child welfare) are important resources to support young people’s full developmental potential (Ungar, 2013). However, system involvement itself (e.g., child welfare, juvenile justice) may interfere with healthy development (Hirsch et al., 2018). Further, silos in service delivery can lead to gaps in MH services across systems. Our study will explore experiences of children in two public systems (child welfare, education). Drawing on a resilience lens, which highlights young people’s capacity to adapt successfully to significant challenges to their development (Masten, 2011) and focusing on navigation of service systems, we will use latent class analysis (LCA) to: a) examine patterns of MH services access across systems; b) test demographic predictors of service experiences; and c) assess associations with later achievement as a proxy of child competence. We hypothesize that latent classes will emerge characterized by varying levels of service access and use, and that these experiences will be differentially related to achievement.

Our sample comes from an integrated data system, including educational and child welfare data from 2000-2018. Inclusion criteria include 6th grade students in 2016-2017 who were assessed with a MH condition. (Due to COVID-19 work-from-home policies, data, which are stored on a secure server and accessible only on-campus, were not accessible prior to abstract submission. However, we have since been granted permission to return to campus to access the data and do not expect further delays.) To link child welfare and educational data, a probabilistic matching software will be used to identify complete and partial matches on identifiers (full name, date of birth). Partial matches will be manually reviewed following decision rules.

LCA indicators will include service measures from educational data (special education evaluation status, service hours) and from child welfare data (removal for MH reasons, receipt of MH case management). Covariates will include race-ethnicity, birth-assigned sex, homeless status, and free/reduced lunch eligibility. Finally, the distal outcomes will be next year (2017-2018) reading and math assessments. LCA will be conducted in Mplus (v. 8) following three steps (Collins & Lanza, 2010): 1) identify the best-fitting unconditional LCA model using common fit indices (AIC, BIC, ABIC), conceptual meaning, and parsimony; 2) integrate demographic covariates as predictors of class membership; and 3) examine distal outcomes of class membership. If LCA models do not converge, we will use a path model to examine MH service use predicting next-year reading and math assessments.

This study uses cross-system and person-centered approaches to explore children’s navigation of and access to needed services, while considering antecedents, and outcomes using a resilience framework. Implications for policy and practice include linking patterns of cross-system services and recommendations for improving access and continuity of MH services. This study provides integral insight into the patterns of mental health service receipt that may support the resilience of children served by multiple systems.

Authors

• Maura Shramko, University of Minnesota - Twin Cities
  Presenting Author

• Cara M. Lucke, University of Minnesota - Twin Cities
  Non-Presenting Author

• Kristine Piescher, University of Minnesota - Twin Cities
  Non-Presenting Author