The Brain Medicine Fellowship: A Competency-Based Training Program to Treat Complex Brain Disorders
Bibliographic record
Abstract
PROBLEM: Complex brain disorders involve symptoms in the domains of affect, behavior, and cognition. It is increasingly recognized that there is a need for a novel type of physician who can treat individuals with these conditions in an interdisciplinary fashion to best address their complexity. Few training programs have focused on the education of such practitioners. APPROACH: The authors outline the development and practices of the Brain Medicine Fellowship, an innovative, competency-based fellowship program at the University of Toronto Temerty Faculty of Medicine that accepts trainees from multiple brain medicine-related specialty training programs to develop expertise in integrative assessment and treatment of complex brain disorders. The authors describe how brain medicine competencies were generated, the current assessment process, and the seminal clinical experience associated with the fellowship-the Brain Medicine Clinic-and explain how it exemplifies brain medicine in action. OUTCOMES: The first fellow was registered from July 2019 to December 2020. As of December 2022, 3 fellows have entered the program, with 3 more anticipated to begin in July 2023. More than 26 supervisors are associated with the fellowship, who offer a diversity of experiences for fellows to choose from in developing their individualized learning plans. The Brain Medicine Fellowship not only fosters the development of a novel type of clinician (a brain medicine specialist) but also is innovative in its educational design as one of the first nonsurgical fellowships to implement competency-based medical education and has resulted in original clinical programming in the form of the Brain Medicine Clinic, which benefits patients and their caregivers. NEXT STEPS: The development of the Brain Medicine Fellowship continues with competency refinement and translation into entrustable professional activities and constituent milestones. A comprehensive program evaluation will be completed by 2025.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".