122 Evaluating the impact of the Translational Research Program at the University of Toronto
Bibliographic record
Abstract
OBJECTIVES/GOALS: There is a need to develop a workforce of translational research professionals with the skills to innovate, mobilize, and commercialize research for unmet needs in the Canadian health system. The objective of this study is to evaluate the impact and value of the master’s degree on the Translational Research Program (TRP) alumni. METHODS/STUDY POPULATION: This study will use a cross-sectional approach and an electronic survey will be administered to alumni. The TRP was established in 2015 and has graduated over 150 students since its inception. Participants will be recruited through convenience sampling via email, social media platforms, and personal communication. Eligible participants must have a conferred Master of Health Science in Translational Research from the University of Toronto. All collected data will remain anonymous and include demographic information about graduation year, race, ethnicity, gender, and employment status. Descriptive statistics will be used to analyze and report the findings. RESULTS/ANTICIPATED RESULTS: The results of this survey will be used to evaluate how the TRP graduate degree helps alumni contribute to healthcare, learn to think differently, and establish their professional networks. The findings will also be used to inform curriculum improvements, enhance competency-based assessments, and understand demographic differences in student cohorts to promote equity, diversity, and inclusion. Investigating the perspectives of alumni reflecting on their degree will support validating the program’s objectives and advance the integration of translational science principles in the healthcare workforce and community. DISCUSSION/SIGNIFICANCE: This research addresses the need to evaluate health sciences education to ensure the program’s novel pedagogical approaches are equipping the next generation of health professionals with the skills to accelerate the transformation of discoveries into 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 | 0.047 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".