Related Experiment Video
Updated: Jul 13, 2025
05:15
The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
Published on: February 19, 2018
10.9K
State-level policies and reunification: A multi-level survival analysis
Catherine A LaBrenz1, Lacey Jenkins1, Hui Huang1
1The University of Texas at Arlington School of Social Work, 501 W Mitchell Street, Arlington, TX 76019, United States of America.
Child Abuse & Neglect
|October 16, 2023
Summary
State child welfare policies significantly impact reunification rates. Policies like caseworker stipends and in-home services improve reunification, while master's degree requirements may hinder it.
Area of Science:
- Child Welfare Research
- Public Policy Analysis
- Family Reunification Studies
Background:
- Limited research exists on the impact of state child welfare policies on reunification outcomes.
- Reunification is the primary goal for children in foster care, yet policy influences are understudied.
Purpose of the Study:
- To examine the relationship between state child welfare policies and reunification.
- To analyze the influence of macro-level factors on reunification, controlling for child-specific factors.
Main Methods:
- Utilized data from the 2016-2019 Adoption and Foster Care Analysis Reporting System (AFCARS).
- Employed multi-level survival analyses to assess state and child factors affecting time to reunification for children entering foster care in 2016.
Main Results:
- States with Title IV-E stipend programs for caseworkers showed increased reunification hazards (HR=1.21).
- States offering statewide in-home post-reunification services also demonstrated higher reunification hazards (HR=1.63).
- States requiring a master's degree for caseworkers exhibited lower reunification hazards (HR=0.72).
Conclusions:
- State policies play a crucial role in child welfare reunification.
- Findings underscore the need to consider policy implications for improving reunification practices and outcomes.
Related Concept Videos
01:20Comparing the Survival Analysis of Two or More Groups
201
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
201
01:15Assumptions of Survival Analysis
136
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
136
01:19Survival Tree
88
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
88
01:09Truncation in Survival Analysis
214
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
214
01:18Introduction To Survival Analysis
251
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
251
01:21Cancer Survival Analysis
357
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
357