Child, Youth and Family Services: Child Welfare, Family Resource Centers, and Violence Prevention
State-Level Paid Family Leave Policy Project
Mission Analytics is working with the Office on Women’s Health (OWH) to explore the effects of state-level paid family leave (PFL) programs on the physical and mental health status of women within a year after childbirth. The project will focus on women in the four states where state-level PFL policies have been implemented. While existing research indicates that PFL policies can support the well-being of children and families, the purpose of this study is to further explore the specific effects of PFL on women’s health behaviors and outcomes, with an emphasis on state-level rather than employer sponsored programs. The project will also examine how the health effects of these state-level policies may affect women’s well-being and their ability to fulfill their roles in the workplace, family or community. The aim of this project is to provide evidence related to the potential health benefits of state-level PFL for women in order to inform recommendations for national paid family leave policy options that ultimately may benefit working women, their families, and their employers.
Planning and Evaluation for AB1326, SB701, AB833, and AB2368 for San Mateo County, San Francisco County, Alameda County, Contra Costa, Marin County, Santa Clara County, Sonoma County, and Santa Cruz County Individualized Child Care Subsidy Pilots
Mission Analytics provides planning support and evaluation for four child care pilot projects: the San Mateo County Child Care Partnership Council, the San Francisco Child Care Planning and Advisory Council, the Alameda County Early Care and Education Planning Council, the Contra Costa County Office of Education, the Marin County Office of Education, the Sonoma County Office of Education, the Santa Clara County Office of Education and Local Early Education Planning Council, and the Santa Cruz County First 5. The pilots allow these seven Counties to address two fundamental concerns: first, that families barely earning enough to meet the high cost of housing in the counties are nevertheless considered too high income to qualify for child care subsidies; and second, that the state reimbursement rates for providers contracted to provide high quality child care are so low that providers cannot cover their costs. Evaluation criteria include the retention of contracted providers, income growth and child care stability for families, and the increase in number of children served in funded child care slots.Exploring the Relationship between the California Paid Family Leave Program and the Well-Being of Low-Income Families
Mission Analytics worked with the Assistant Secretary for Planning and Evaluation (ASPE) to provide assistance in recruiting for and conducting focus groups with low-income mothers of young children in California who are eligible for the California Paid Family Leave (PFL) program. Through approximately four focus groups, this qualitative study sought to explore the experiences of low-income mothers who worked prior to birth. It addressed: their knowledge about and experiences with PFL, access to other resources to support their families, actions taken related to work and childcare immediately after childbirth, attachment to work and pre-birth employers, and perceptions about use or non-use of PFL.
Evaluation Services on Data matching and Analysis of Administrative Data for San Francisco Department of Children, Youth, and their Families
Since 2015, Mission Analytics has been providing administrative evaluation services to the San Francisco Department of Children, Youth, and Their Families (DCYF). Project activities include management and analysis of youth surveys, matching Juvenile Justice Information System (JJIS) data with Contract Management System (CMS) records, delivering program-level year end evaluation reports including youth survey summaries, generating outcome measures mandated by the state for the Juvenile Justice Crime Prevention Act, and supporting ad hoc efforts. Under this contract, we contributed to the equity analysis for the Community Needs Assessment (CAN), generated summary statistics for justice-involved youth for the Multi-agency Local Action Plan. We continue this evaluation work under the present contract, which also includes a longitudinal analysis of CMS data and a youth survey overhaul.
Family Leave and Lower Income Families Linkages between Mothers’ Return to Work Leave and Child Care
Mission Analytics worked with the Assistant Secretary for Planning and Evaluation (ASPE) to recruit and conduct approximately nine focus groups with low-income mothers with young children who participated in the Paid Family Leave (PFL) programs in California, New Jersey, and Rhode Island. To better understand factors that facilitate lower income mothers’ return to work following childbirth, this project analyzed the relationship between returns to work after childbirth, the use of PFL, and the role of formal and informal (e.g. care by family members) child care.
Family Resource Center Initiative Evaluation
Mission Analytics served as the evaluator for the Family Resource Center (FRC) Initiative undertaken by the First Five Commission of the City and County of San Francisco. Implemented in 2009, the Initiative aims to improve the provision of social services to low-income San Francisco families, especially in the domain of parenting skills. Mission’s activities included data analyses linking demographic, geographic, and participation information from the 22 funded FRCs to child welfare data. The analysis produced databooks for the each of the FRCs, an initiative-wide description of participation patterns, and an evaluation of the impact of participation on child welfare outcomes (reunification, subsequent maltreatment), parenting skills, and family risk profiles.
Santa Clara County Children of Color Project and the California Partners for Permanency Project (Child Welfare)
Mission Analytics worked with Santa Clara County Social Services Agency, Department of Family and Children's Services (DFCS) on the agency’s Children of Color Project (COC) and on its California Partners for Permanency Project (CAPP). The primary objectives of the Children of Color Project were 1) to explain why children of color are over-represented in the child welfare caseload in Santa Clara County, and 2) identify ways to address this disparity. The CAPP sought to identify ways to limit children’s tenures in the child welfare caseload, and, to the extent possible, ensure that children exit the caseload for permanent placement. For both projects, Mission Analytics used data from the California Child Welfare Services Case Management System (CWS/CMS), the Comprehensive Assessment Tool (CAT), and the CalWORKs program. For the Children of Color Project, Mission Analytics used these data sources to model the likelihood that DFCS will substantiate an allegation of abuse, open a case after substantiating an allegation, and take a child into out of home placement. Mission Analytics used these models to determine whether DFCS is more likely to take any of these actions with respect to children of color than for otherwise identical White children. For the CAPP, Mission Analytics used the same data sources to identify factors that are associated with permanent placement and with the durations of placement episodes, and that DFCS can affect.
Violence Prevention and Intervention Initiative and Youth Workforce Development-High Risk Youth Evaluation
For the San Francisco Department of Children, Youth, and Their Families (DCYF) Mission Analytics served as the evaluator for the San Francisco Violence Prevention and Intervention Initiative and the Youth Workforce Development (High-Risk) program. The mid-period evaluation report linked data from the DCYF Contract Management System and the Juvenile Justice Information System (JJIS) for the San Francisco Juvenile Probation Department to identify the characteristics of youth and young adults served by the programs, success in reaching juvenile-justice involved youth, and outcomes in terms of job placements and subsequent contact with the juvenile justice system. The final report expanded to additional data sets, such as school district data and community behavioral health. The evaluation drew on statistical modeling and descriptive techniques to identify the activities and combinations of activities associated with lower rates of violent behavior.