Joan R. Rycraft

Adapting systems of care for child welfare practice with immigrant Latino children and families

Recent reform efforts in the child welfare system have called for child welfare agencies to provide community-based services and to increase the involvement of external stakeholders in identifying and developing services within the community. At the same time, child welfare agencies are faced with the challenge of providing services to an increasingly diverse population of children and families. As a result, there is a need for evidence-based practice models that respond to these challenges and promote positive outcomes for children and families. This paper describes the development of a program designed to train child welfare staff on the application of an existing evidence-based framework, systems of care, to practice with immigrant Latino children and families as a means of responding to these multiple calls for systems change and practice improvement. Immigrant Latino children and families represent the largest and fastest-growing population in the United States, and thus require the attention of child welfare systems and the development of evidence-based practices designed to respond to the unique needs of this population. Recommendations for program planners and evaluators on the application of systems of care to child welfare practice with immigrant Latino children and families are provided.

Building a model to predict caseworker and supervisor turnover using a neural network and logistic regression

Abstract Human service professionals are increasingly pressured to use sophisticated data analysis tools to support service decisions. However, the application of these tools often involves assumptions and nuances that are difficult for the practitioner to evaluate without specialized information. This article helps the practitioner evaluate two different quantitative methods, a logistic regression and a neural network. Both were used on the same data set to develop a model for predicting employee turnover in a regional child protective services agency. The different steps of building and enhancing the model were discussed. Ultimately, the neural network was able to predict turnover more accurately than a logistic regression by only 1%. The article provides advice to practitioners on comparing, evaluating, and interpreting logistic and neural network tools.

Hurdling the Artificial Fence Between Child Welfare and the Community: Engaging Community Partners to Address Disproportionality

As with many national and state social service systems, child welfare agencies have traditionally functioned in isolation. A multitude of federal and state laws and policies direct the functions and practices of child welfare systems, setting up an artificial fence around the agency that has created a culture lacking in interprofessional collaborations. However, recent reform efforts have emphasized the importance of engaging the community in discussions and decisions regarding child welfare practice. Child welfare agencies are now expected to work with community leaders, key stakeholders, affiliated service providers, and families to address issues impacting children and families. A major obstacle is determining where to begin, how to proceed, and what is needed to develop those partnerships. This article reports the efforts of one state agency in hurdling that artificial fence to address the overrepresentation of minority children in the system. Through focus group interviews with community members, child welfare staff, and legal professionals, barriers to community engagement are identified, and recommendations are provided for facilitating meaningful relationships and partnerships between child welfare agencies and the communities they serve.

The Enhancement Seminar Model as a Strategy to Promote Diversity and Student Success in MSW Programs

This study evaluates the effectiveness of an enhancement program by examining a cohort of 57 students admitted on probationary status to an MSW program in 2002 and required to participate in the enhancement program. The demographics for students admitted on probation demonstrate that the program is effective in increasing the diversity of the student body and that students admitted on probation were very successful in the MSW program when compared to the cohort of 167 students admitted unconditionally.

Using Biofeedback to Enhance Interventions in Schools

SUMMARY The purpose of this paper is to introduce biofeedback technology to the school social worker as an efficacious intervention for stress/stress related disorders in children whether the stressor is behavioral, psychological, or physiological. Biofeedback has few caveats and presents an opportunity to use technology that is at once appealing to the student and reliable for the practitioner. This intervention moves the practitioner away from the medical model of treatment (a crisis model) to a learning model that is conducive to a strengths perspective intervention. A model for successful biofeedback intervention, advantages, and disadvantages of biofeedback in schools are included in the discussion.

Evaluation of the Infant Adoption Awareness Trainings: Transforming Training Knowledge to Adoption Practice

Mixed methods were used to evaluate the effectiveness of the infant adoption awareness trainings. Quantitative data were collected through a pre-test/post-test design (n = 2797) that measured trainees’ level of knowledge gained from the trainings. Qualitative data were collected through a follow-up survey (n = 304) and telephone interviews (n = 82) with trainees to assess whether the learning that occurred at the trainings has been applied to adoption-related practices. Findings suggest that trainees felt more comfortable discussing adoption as an option and were better prepared to use correct adoption terminology. Implications for practice, policy advocacy, and future research are discussed.