Grant: Applied Research Rapid Response to COVID-19
Seneca College of Applied Arts and Technology
$64,631.00
Machine learning and blockchain-backed optimized assignment matching for PSWs to improve understaffing_x000d_ and risk during the COVID-19 outbreak
FY 2020-21
Summary
The COVID-19 pandemic exposes systemic weakness in how the Personal Support Worker (PSW) industry is operated here in Canada. Lack of validated proof of certifications and microcredentials (first aid, safety training, background checks, etc.) leads to hiring delays, understaffing, and potential risks to patients. This in turn causes issues for PSWs, including low pay, inefficient scheduling, underemployment, and has resulted in an unsustainable PSW "gig economy." TriNetra is collaborating with ConnexHealth to implement a system for verifying PSW qualifications, achievements, and certifications. They are proposing to collaborate with Seneca to extend the features of this system to include capabilities to match candidates to job assignments based on their entire profiles, including certifications, training, geography, work history, and availability. This will help the COVID crisis by reducing understaffing at care facilities due to difficulty in validating the credentials of potential workers, eliminating travel and skills mismatches between PSWs and their given assignments; and improving PSW quality of life through better assignment matching, recognition for training, and less travel.
This project will initially create a full-featured PSW community portal with blockchain-based credentialing and a machine learning/artificial intelligence-based matching system to address the current need for better and more reliable credentialing and placement software for PSWs. The portal will automatically create individual PSW assignments and schedules to encompass all aspects of their training, availability, and geography. The first two phases of the project will result in a requirements report and optimized matching system to be deployed within the first 12 weeks of the project, and a fully functional system at completion of the 24 week project. This would provide immediate benefit to help reduce understaffing in long-term care facilities and assist PSWs in faster employment at better-matched assignments.
Program
Applied Research Rapid Response to COVID-19
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
554458-2020
Name nom
Leaver, Chad
Department département
Head Office
Organization id
1803
Institution établissement
Seneca College of Applied Arts and Technology
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2020
Award amount
64631
Program id
COVPJ
Program name en
Applied Research Rapid Response to COVID-19
Program name fr
Recherche appliquée en réponse rapide à la COVID-19
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
1597
Committee name en
College and Community Social Innovation Fund
Committee name fr
Programme d'innovation dans les collèges et la communauté Fonds d'innovation sociale
Area of application code
1101
Area of application group en
Health, education and social services
Area of application group fr
Santé publique, éducation et services sociaux
Area of application en
Human health (including medically-related psychological research)
Area of application fr
Santé publique (y compris recherche psychologique liée à la médecine)
Research subject code
2805
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Learning and inference theories
Research subject fr
Théories de l'apprentissage et l'inférence
Installment
inst:1 - 1
Partie
1
Nb partie
1
Application title
Machine learning and blockchain-backed optimized assignment matching for PSWs to improve understaffing_x000d_
and risk during the COVID-19 outbreak
Keyword
NSERC
Application summary
The COVID-19 pandemic exposes systemic weakness in how the Personal Support Worker (PSW) industry is operated here in Canada. Lack of validated proof of certifications and microcredentials (first aid, safety training, background checks, etc.) leads to hiring delays, understaffing, and potential risks to patients. This in turn causes issues for PSWs, including low pay, inefficient scheduling, underemployment, and has resulted in an unsustainable PSW "gig economy." TriNetra is collaborating with ConnexHealth to implement a system for verifying PSW qualifications, achievements, and certifications. They are proposing to collaborate with Seneca to extend the features of this system to include capabilities to match candidates to job assignments based on their entire profiles, including certifications, training, geography, work history, and availability. This will help the COVID crisis by reducing understaffing at care facilities due to difficulty in validating the credentials of potential workers, eliminating travel and skills mismatches between PSWs and their given assignments; and improving PSW quality of life through better assignment matching, recognition for training, and less travel.
This project will initially create a full-featured PSW community portal with blockchain-based credentialing and a machine learning/artificial intelligence-based matching system to address the current need for better and more reliable credentialing and placement software for PSWs. The portal will automatically create individual PSW assignments and schedules to encompass all aspects of their training, availability, and geography. The first two phases of the project will result in a requirements report and optimized matching system to be deployed within the first 12 weeks of the project, and a fully functional system at completion of the 24 week project. This would provide immediate benefit to help reduce understaffing in long-term care facilities and assist PSWs in faster employment at better-matched assignments.
