Submission ID 77850
| Code | OH-2-5 |
|---|---|
| At the end of this workshop, participants will be able to: | |
| Category | Medical Education |
| Type | Oral |
| Will the presenter be a: | Other |
| Presenter Other | Research Program Manager |
| Title | Modelling the Multiverse of Medical Education: Findings From the Ontario Medical Schools Outcome Measures Research Consortium (Omsomrc) |
| Background/Purpose | At the foundation of effective medical education 'big data' scholarship is data sharing between institutions across the trajectory of training. We report findings from a data sharing collaboration consisting of the six undergraduate medical programs in Ontario, the Medical Council of Canada (MCC) and the Canadian Residency Matching Service (CaRMS). We considered associations between admissions and assessment variables and performance on the MCC Qualifying Examination Part 1(MCCQE1) -a first step to licensing for physicians in Canada. We present preliminary outcomes and lessons learned through project development. |
| Methods | Data for learners from the 6 medical schools who wrote MCCQE1 between 2015 and 2017 were included in the analyses (n= 2668). With MCCQE1 scores as an outcome, a stepwise hierarchical model was used with five steps of predictor variables (learner demographic characteristics, admissions variables, pre-clinical training assessments, OSCE performance, and clinical training assessments). The final models that explained the greatest amount of variance in MCCQE1 scores were retained. |
| Results | Across the schools the determination of best models differed by step of predictor variables. The variance explained ranged from 32% to 60%. Each school's final model had variation in identifying significant predictors of MCCQE1. However, across the schools, clerkship variables were found to be the best predictors of MCCQE1 performance. |
| Discussion | Data sharing collaborations can reveal variation between education programs, offering an opportunity for learning from each other and leveraging best practices. We discuss challenges and opportunities for future research. |
| Keyword 1 | Predictors |
| Keyword 2 | Medical Students |
| Keyword 3 | Big data |
| Abstract content most relevant to: (check all that apply) | Undergraduate Medical Education |
| Abstract Track - First Choice | Learning Outcomes |
| Learning Outcomes | General |
| Authors | Ilona Bartman Lawrence Grierson Saad Chahine Kulamakan (Mahan) Kulasegaram Archibald Douglas Erin Cameron Brian Ross Peter Wang Amrit Kiripilani Cassandra Barber Eleni Katsoulus John Hogenbirk Claire Touchie Raquel Burgess |
