In the News

A Simulator that Predicts the Impact of Social Service Policy

Interview by Tressa Fox, Vancouver,  30-May-2016

University of Toronto researchers are developing a simulation tool that will predict the impact of social service policies on vulnerable populations.

Researchers at U of T’s Centre for Social Services Engineering are exploring innovative technologies designed to improve the way social services are evaluated and delivered to vulnerable populations. Of particular interest are the homeless, at-risk youths, seniors and families living below the poverty line. The Centre is one of the first of its kind to apply engineering techniques and methodologies to the social services sector.
 
One of the Centre’s groundbreaking projects is a computer simulator that can model a city’s network of social services and track specific groups of clients as they make decisions and move through a simulated social support system. The multi-disciplinary research team supporting this project includes U of T and Centre faulty members Dr. Vicky Stergiopoulos, Assoc. Professor & Director, Department of Psychiatry and Professor Marion Bogo, Factor-Inwentash Faculty of Social Work.
 
I recently spoke with the Centre’s director Dr. Mark Fox (MF), Dr. Michael Gruninger (MG) and PhD candidate Bart Gajderowicz (BG), the engineering research team spearheading the Social Services Simulator project.
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Dr. Fox, what is your vision for this project?

TF
MF

After reviewing the literature, it became clear that policy makers need better tools to assess the outcome of social service policies                 and programs. To this end, we decided to develop a simulation tool that would help government leaders evaluate the impact of a                   policy or a program before it’s subject to live trials.




What prompted your Centre to pursue this particular project?

TF
MF

Changes in social service policies and programs can have profound implications for social agencies and the clients they serve. Policies       are often based on theories or practices that haven’t been rigorously tested. To further complicate this picture, social service clients are used as test subjects, a practice that not only raises ethical questions, but that can result in very negative consequences, especially for vulnerable populations like the homeless. 
 
There are also fundamental challenges with live trials, especially for long term studies. When participants are selected for a study,       they’re often a mix of people without enough statistical significance to draw conclusions. I’ll give you an example: a study of 100 elderly    men who were chronically homeless failed due to the unstable nature of the test subjects. During the study, 20% of the subjects died. Another 5% dropped out and could not be located. By the end of the study, only 11 men remained. Transient lifestyles and movements in and out of the system further complicate tracking and monitoring the homeless.  These obstacles are not an issue in a simulation test environment.

Let’s say a particular study finds that a program for people living below the poverty level is 75% successful. For the 25% that the program fails, the consequences can be devastating.
 
A good example of this is the “Housing First” initiative that began in NYC in the 90’s.  Instead of placing homeless candidates on a one or two year plan to prepare them for permanent housing, clients were given rent subsidies and apartments right away.  No conditions were placed on them, like drug testing, and they received social work support on a regular basis. While a majority benefited and remained in their residences, 25% returned to the streets. In Canada, studies revealed a 25-40% fail rate for similar programs.
 

So the questions we propose are “How were individual subpopulations impacted by this program? Who are the homeless within the 25-40% that failed to benefit from the program?  When and why did they drop out of the program, and how were they impacted by this experience?” How were the elderly impacted? How were elderly men versus elderly women impacted; singles versus families, and so forth.
 
We need to understand how these individuals will be impacted before we implement a program across an entire homeless  population. 


TF

You mention negative consequences that result from live trials. Can you provide an example?

MF
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