| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | August 8, 2020 |
| Latest Amendment Date: | August 8, 2020 |
| Award Number: | 1951896 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Sylvia Spengler
sspengle@nsf.gov (703)292-7347 CNS Division Of Computer and Network Systems CSE Direct For Computer & Info Scie & Enginr |
| Start Date: | October 1, 2020 |
| End Date: | September 30, 2023 (Estimated) |
| Total Intended Award Amount: | $140,421.00 |
| Total Awarded Amount to Date: | $140,421.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
175 FOREST ST WALTHAM MA US 02452-4713 (781)891-2660 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
175 Forest Street Waltham MA US 02452-4705 |
| Primary Place of Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | S&CC: Smart & Connected Commun |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
This project lays the groundwork for developing predictive models and improving work processes in social agencies that serve underserved populations. The specific targets of the work are long term homelessness and recidivism (tendency to relapse), with a primary focus on the city of Boston, Massachusetts in collaboration with a community partner (Pine Street Inn). The planned integrative research includes: predictive modeling to assess needs of the homeless guests to improve the match against the right services, and process modeling to improve triage work at the homeless shelter. The project will identify data necessary for the modeling efforts and resolve data-related problems such as data quality and completeness; and assess current triage practice to develop and experiment with new versions of work processes at a homeless shelter.
This project will advance current knowledge about work practices and service matching at homeless shelters, and improve understanding of data availability and quality to support these outcomes. The work will develop ways to better serve the homeless population as well as lay the foundations to roll out the models and results to other communities in Massachusetts as well as other states and other underserved populations, such as at-risk families, populations suffering from disabilities, and those experiencing substance abuse or addiction. Significant societal benefits can accrue through improved and efficient support for the homeless, either directly through services or indirectly through better use of federal and local taxes. Providing services that match the needs of the homeless, and improving the work practices at the shelter can reduce both the length of stay and recidivism, contributing to the overall quality of life within society.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project focused on building relationships with community groups to support homeless individuals and launch a research study. Our goals were to identify main issues like reducing shelter stay lengths and preventing recidivism (return visits to the shelter), and to find solutions through better data use and operational changes. Using data from the Homeless Management Information System, we found that race, veteran status, disability, and age are significant factors in prolonged homelessness. Specifically, women, elderly, and those identifying as Hispanic, Asian, or Black African face longer shelter stays, with 76% having at least one disability. Recidivism was also a major cause of extended stays.
We studied the unique challenges of managing homelessness services, which differ significantly from standard processes due to their complexity and the need for specialized knowledge. Our interviews with individuals experiencing recidivism at Pine Street Inn revealed key issues like uncertainty, the need for support, safety, addiction, and isolation.
The project emphasized the importance of high-quality data and better operational processes for effectively tackling homelessness. Making progress along these directions can set the foundation for creating targeted programs and operational improvements that can decrease shelter time and recidivism, to significantly improve the lives of those affected by homelessness.
Last Modified: 02/11/2024
Modified by: Monica J Garfield
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Process model for intake processes at homeless shelterPurao, S., M. Garfield. 2020. Process Modeling in Humanitarian Settings: A Case Study and Lessons Learned. European Conference on Information Systems. Morocco. Virtual Conference.Creative CommonsMonica J GarfieldProcess model for intake processes at homeless shelter
Prototype of Intake tool to capture possible risk of long term homelessnessPurao, S., M. Garfield, X. Gu, and P. Bhetwal. 2019. Predicting the Slide to Long-Term Homelessness. IEEE Conference on Business Informatics. Moscow, Russia.Public DomainMonica J GarfieldIntake tool to capture possible risk of long term homelessness
Final regression model to identify attributes that signal possible long term homelessnessPurao, S., M. Garfield, X. Gu, and P. Bhetwal. 2019. Predicting the Slide to Long-Term Homelessness. IEEE Conference on Business Informatics. Moscow, Russia.Public DomainMonica J Garfieldattributes that signal possible long term homelessness
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