96 Evidence to impact: Developing a workforce of translational research professionals
Bibliographic record
Abstract
OBJECTIVES/GOALS: The goal of the Translational Research Program (TRP) at the University of Toronto is to provide structured and adaptive competency-based training around the translation, mobilization, implementation, and commercialization of research for the current and future Canadian healthcare workforce. METHODS/STUDY POPULATION: Guided by the Toronto Translational Framework, the TRP is a two-year hybrid master’s degree program that integrates courses, case-studies, mentorship, and experiential learning to facilitate real-world student-led translational projects. Focusing on skills development and competency-based assessment, the curriculum emphasizes ongoing reflection, interprofessional collaboration, and multidisciplinary problem-solving using human-centered principles. Learners identify problems using contextual inquiry to define unmet needs and frame design requirements. Systematic ideation is used to generate, select, and validate promising concepts for further iterative prototyping and evaluation. RESULTS/ANTICIPATED RESULTS: Throughout the program, students demonstrate a range of collaborative skills and activities around developing, assessing, and implementing new health interventions. Learners apply the Toronto Translational Framework and refine their professional competencies during the final year of the program in a student-led Capstone project. The unconventional combination of a guided framework and a learner-driven curriculum has produced over 120 graduates in a variety of careers within government, industry, clinical settings, and start-ups. The program’s focus on problem-solving and lifelong learning is growing Canada’s translational workforce and advancing translational health science education. DISCUSSION/SIGNIFICANCE: The TRP addresses the need to educate healthcare professionals in Canada about translational research and accelerate the transformation of scientific discoveries into tangible interventions that benefit human health, improve clinical medicine, and enhance patient care.
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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 | 0.242 | 0.367 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".