93 Empowering the Next Generation of Clinical & Translational Scientists
Bibliographic record
Abstract
OBJECTIVES/GOALS: Biomedical research fields are facing the challenges of demand for skilled workers as well as challenges related to diversity in that workforce. It is important that the healthcare workforce reflect the population it serves. The Exposures Internship seeks to address this by building pathways for youth to pursue careers in research and medicine. METHODS/STUDY POPULATION: In 2021, the Yale Cultural Ambassadors expressed concern about the lack of free high quality, educational offerings for youth that summer. They asked YCCI to consider developing a summer program for students aged 15 and older that focused on spurring interest in careers in healthcare, medicine, and clinical and translational research. The result was a 4-week virtual learning experience for 34 interns who met daily via Zoom and participated in course work, lectures, journal clubs, group projects, and virtual lunches with internationally renowned clinical research and healthcare leaders. Sessions were designed to help interns gain knowledge of and exposure to current topics in clinical and translational science and to observe the various steps of proposing, designing, undertaking, and analyzing clinical trials. RESULTS/ANTICIPATED RESULTS: YCCI received over 900 inquiries from around the world with more than 200 completed applications for participation in the internship for the pilot year. Since then, YCCI leadership has worked with community partners to engage young scholars from 17 different states, Canada, Mexico and Puerto Rico. Of those, we estimate 75% are minority, ~50% female and 20% from rural areas with limited similar opportunities. During the four weeks of the program these highly motivated students worked on projects aimed at increasing participation in pediatric research through a revised Informed consent and adolescent assent process and a youth centered awareness campaign. Interns were so inspired that they requested the program be continued beyond the initial four weeks. As such, YCCI continued to offer sessions throughout the year. DISCUSSION/SIGNIFICANCE: In evaluation of the pilot program 95% of respondents strongly agreed that the program exposed them to new information about clinical and translational research. One intern shared, This program has unquestionably made me consider becoming a researcher in the future with the goal of becoming a principal investigator within my interest in medicine.
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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 | 0.032 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.067 | 0.026 |
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".