Karen Weyman
Biographic Data
| ID | 7804480 |
|---|---|
| NAME | Karen Weyman |
| GIVEN NAMES | Karen |
| FAMILY NAME | Weyman |
| SIGNATURE | WEYMAN K |
| AFFILIATIONS | is associate professor, St. Michael’s Hospital, Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada. |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Testing regular expression searches and machine learning models to determine housing instability and low income status from primary care electronic medical record data in Toronto, Ontario
While the machine learning approach demonstrated reasonable PPV, the overall metrics were poor and unlikely to be useful in a clinical setting for identifying patients with housing or economic needs. More robust analysis could be explored, but continued patient-captured SDoH information is necessary
Same but Different
PURPOSE: Longitudinal integrated clerkships (LICs) are a widely used method of delivering clerkship curricula. Although there is evidence that LICs work and core components of LIC training have been identified, there is insufficient understanding of which components are integral to why they work. To address this question, this research explored how students experienced the first year of an LIC program. The aim was to use participants' understandi…
No prominent works on this page.
Same but Different
PURPOSE: Longitudinal integrated clerkships (LICs) are a widely used method of delivering clerkship curricula. Although there is evidence that LICs work and core components of LIC training have been identified, there is insufficient understanding of which components are integral to why they work. To address this question, this research explored how students experienced the first year of an LIC program. The aim was to use participants' understandi…
Testing regular expression searches and machine learning models to determine housing instability and low income status from primary care electronic medical record data in Toronto, Ontario
While the machine learning approach demonstrated reasonable PPV, the overall metrics were poor and unlikely to be useful in a clinical setting for identifying patients with housing or economic needs. More robust analysis could be explored, but continued patient-captured SDoH information is necessary
Biostatistics (1 work) · Chronic Disease Management Strategies (1 work) · Curriculum (1 work) · Electronic health record (1 work) · Electronic medical record (1 work) · Epidemiology (1 work) · Food Security and Health in Diverse Populations (1 work) · Health disparities and outcomes (1 work) · Innovations in Medical Education (1 work) · Interprofessional Education and Collaboration (1 work)