An Environmental Scan of American and Canadian Translational Science Training Programs at the Graduate Level
Bibliographic record
Abstract
Abstract To date, there are considerable delays in bringing academic innovations into clinical practice. In part, this is due to a lack of knowledge translation and communication between clinicians and scientists. While MD/PhD programs could bridge this gap, more inclusive and sustainable alternatives must be explored. In the United States, the Howard Hughes Medical Institute (HHMI) launched an initiative to create programs wherein graduate students would be exposed to clinical curricula and establish networks with health professionals. In this study, we aim to survey such programs in North America and identify key features. In our environmental scan, we analyzed the translational science training curricula of 28 American and 17 Canadian universities. We observed that 25 schools in the United States offered training in translational science at various degree levels (certificate, Master’s, PhD, etc.) whereas only 4 Canadian institutions did so and primarily at a Master’s level. From those programs, 5 American universities offered a multi-faceted training program that met at the intersection of courses, clinical mentorship, and networking opportunities compared to only 1 in Canada. Therefore, while we noted a growing interest in science translation programs in the United States, there is a current lack of such programs at Canadian institutions. Based on the need established by this environmental scan, we hope to establish a translational science certificate program at McGill University that fills this training void and paves the way for other universities across Canada.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.023 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".