Start Date
23-4-2026 12:00 AM
Description
Nonprofit organizations are essential in supporting youth's mental health in their communities, and the data they collect allows us to evaluate program effectiveness and how services can be improved to better support their communities. This project aims to evaluate program and provider outcomes, client demographics, and assessment scores for a regional nonprofit and family services organization to better understand client success, program effectiveness, and the impact of COVID-19. The dataset includes client demographic information, programs, zip codes, first and last session assessment dates and scores, discharge types, and provider IDs. To investigate score changes, we are analyzing the correlation between the two scoring measures that the agency uses, either client or provider reported. Additionally, we are aiming to find the best indicators for score increases by demographic information such as gender, race, and zip code of residence or pandemic related changes. We have found that during the Covid period, the agency was serving significantly less clients than in years prior and after. We have also found a smaller correlation than expected between the two scoring metrics and are actively investigating what this means. We also aim to recognize differences among client subgroups such as zip codes, races, or genders.
Research Highlights
The Problem: Researchers aimed to evaluate the clinical effectiveness and intake patterns of a regional nonprofit providing counseling to youth and families in crisis within the St. Louis metropolitan area.
The Method: The study analyzed a dataset of 26,792 entries for 21,114 youth clients served between 2013 and 2025, utilizing multilevel models to predict Outcome Rating Scale (ORS) and Children’s Global Assessment Scale (C-GAS) scores based on variables like race, gender, and provider ID.
Quantitative Finding: 92.3% of clients reside in St. Louis or St. Charles County; 98.6% identify as male or female; 87.8% identify as White or African-American; client enrollment peaked at 4,347 in 2015 before dropping to 2,554–2,631 during the 2020–2021 COVID-19 pandemic.
Qualitative Finding: The specific provider of care was the most significant predictor of client discharge codes and clinical scores, while demographic variables and length of enrollment were non-significant predictors; client intake consistently surges in August and September aligned with school enrollment cycles.
Recommended Citation
Schrumpf, Alison; Tye, Eleri; and Veninga, Hailey, "An Exploratory Analysis of a Regional Nonprofit & Family Services Organization" (2026). 2026 Student Academic Showcase. 25.
https://digitalcommons.lindenwood.edu/src_2026/Posters/1/25
Included in
An Exploratory Analysis of a Regional Nonprofit & Family Services Organization
Nonprofit organizations are essential in supporting youth's mental health in their communities, and the data they collect allows us to evaluate program effectiveness and how services can be improved to better support their communities. This project aims to evaluate program and provider outcomes, client demographics, and assessment scores for a regional nonprofit and family services organization to better understand client success, program effectiveness, and the impact of COVID-19. The dataset includes client demographic information, programs, zip codes, first and last session assessment dates and scores, discharge types, and provider IDs. To investigate score changes, we are analyzing the correlation between the two scoring measures that the agency uses, either client or provider reported. Additionally, we are aiming to find the best indicators for score increases by demographic information such as gender, race, and zip code of residence or pandemic related changes. We have found that during the Covid period, the agency was serving significantly less clients than in years prior and after. We have also found a smaller correlation than expected between the two scoring metrics and are actively investigating what this means. We also aim to recognize differences among client subgroups such as zip codes, races, or genders.