Grant: Fellowship: POR Awards - Transition to Leadership Stream - Phase 1

Problem: Canada's children are falling behind. More than 1 in 3 are overweight or obese, 1 in 4 have a mental health problem, and 1 in 4 are not developmentally ready for school. Early prevention is the key to reducing the life-limiting consequences of these conditions. Part of this important goal is the development of pediatric clinical prediction algorithms, tools that combine different types of patient information to help clinicians determine whether a child is at risk for a future health condition. While clinical prediction algorithms have the ability to help clinicians and families prevent disease, they also have the potential to widen health inequalities if they are not inclusive of different ethnicities, income groups, and the diverse needs of parents and children. My project will involve parents at every stage of clinical prediction algorithm priority setting, development, and implementation to ensure that algorithms are produced in an equitable and patient-oriented manner. Using TARGet Kids!, a research network that has collected data on 11,000 children in the first 5 years of life, I will initiate the ParEnt and CliniciAn algorithm Leads (PEARL), a group composed of 5 primary healthcare providers and 8 parents from families with diverse ethnic and income backgrounds. The PEARL will help address challenges associated with pediatric clinical prediction algorithms and safeguard their equitable development and use. I will begin by creating a state of the art clinical prediction algorithm for childhood obesity, mobilizing expertise with the PEARL while developing my skills as a patient-oriented research leader. My demonstrated success in research, mentorship, and leadership along with support from my mentors and SPOR-POR PhD Award, will result in the production of novel methods for patient-oriented clinical prediction algorithms that engage parents, support diversity, and optimize the health of all Canadian children.