This piece was originally published in the February 2022 issue of Policy & Practice magazine.
For child welfare leaders, the phrase “workforce capacity” often connotes efforts to improve caseload allocation or caseworker training with an eye toward greater efficiency. The trouble is, child welfare isn’t a system that should make cost savings its goal. Child welfare is a system that needs to focus on enabling better results for the families and children it supports.
In child welfare, arguably the most important aspect of “workforce capacity” is our collective ability to bring empathy, demonstrate compassion and deliver support to all families and children. Historically, our country has not performed well against that metric. Race bias in our work is a demographic fact, and a focus on efficiency improvements will do little or nothing to change that. How can we do the work of eradicating bias and driving racial equity without increasing risks to families and kids? I believe part of the answer is technology tools designed specifically for this purpose.
Virtual reality (VR), which lets caseworkers practice in a realistic way, is one with the greatest potential. Unlike simulation rooms, VR scenarios enable dynamic interactions and a highly personal user experience. When wearing a VR headset, each caseworker is immersed — and on their own — with the people in the virtual environment. Once headsets are off, participants come together as a group to reflect on and learn from their experiences.
Child welfare leaders who are using VR for recruiting, onboarding and training are seeing results in the form of lower turnover. But how might we use this tool in the fight for racial equity?
What can Tory teach us?
Tory is a 13-year-old boy who lives only in virtual reality. Tory identifies as gay, which is creating conflict with his dad, Ben. There may have been some violence between them. Adding to the challenges is Tory’s mom, Cynthia, who is very dysfunctional. While Tory and his family are virtual, their circumstances — and complicated home life — mirror what many experience in real life.
While in the VR headset, the user becomes absorbed in the many facets of Tory’s world. Throughout the scenario, the user chooses from three possible questions to learn more about Tory and his parents. For example, you have a chance to ask Cynthia about her relationship with Ben. You could pose the question as “How’s your relationship with Ben?” or “Is Ben a good husband?” Or you could inquire, “Is Ben Tory’s father?”
Interact with a teen and his family to assess if the home is safe for him. Would their race change your assessment?
At the end of the experience with Tory and his family, users come together to talk through a set of survey questions. Using a scale of one to 10, each participant responds to questions, such as “How angry did you find Cynthia?” and “How threatening did you find Ben?” Only after completing the survey do participants learn there was not one family but two. They have the same names. They live in the same house. They wear the same clothes. And they deliver the same lines.
There's a single difference: one family is Black. The other is white.
These seminars are powerful, eye-opening experiences for participants, and early findings reflect the literature on race bias. For example, users have been more likely to say they see Cynthia as capable of handling the situation when she is Black. That may suggest a greater likelihood of sending help to the white family. Since the scenario provides no income data, users must assess the family’s economic situation based solely on what they see. Even though that’s identical in both versions, users have been more apt to view the white family as poor and the black family as middle class.
Beyond the self-reported scores are the choices users made as they interacted with the family. Each of the sets of three questions includes one that is empathetic (“How’s your relationship with Ben?”); one that is a slight microaggression (“Is Ben a good husband?”); and one that is a high microaggression (“Is Ben Tory’s father?”). A score is generated based on which questions a user chooses to ask. More empathetic behavior yields a higher score.
To be sure, this “empathy score” can be valuable insight for that individual. It’s even more valuable when we look at average scores across wide swaths of users. Among users of all demographic groups, the average empathy score is 17 when talking with the white family. With the Black family, the average is about 10. In other words, in the headset, the Black family is receiving almost half as much empathy. That should give everyone pause — and prompt us to change our mindsets and behaviors.
This tool is not designed to shame or blame or indict anyone. Rather, it's designed to invite all of us into a reflective space where we can begin to design behavior change around this particularly challenging issue.
Achieving race equity will take more than an extra course or a special committee. It is everyone’s job all day, every day, in everything we do. Let’s work together to build capacity for deep and enduring change.