198 Creating a larger, more inclusive cohort to promote scholar engagement through the addition of an invited KL2 seminar fellows program
Bibliographic record
Abstract
Objectives/Goals: The ITHS KL2 Seminar Fellows program creates a larger cohort by inviting additional early career faculty to join the tailored career development curriculum. The implementation of this program seeks to increase collaboration and innovation by amplifying diverse perspectives and increased networking. Methods/Study Population: In addition to the funded KL2 Scholars awarded each year, 13–15 Seminar Fellows are invited to be full participants in the KL2 curriculum, which includes monthly career development seminars and opportunities for feedback on their research. Invited Fellows are early career investigators who were promising KL2 applicants, faculty with alternative career development funding, and/or new underrepresented faculty in Washington, Wyoming, Alaska, Montana, and Idaho. Fellows commit to one year of participation, which can be renewed on a case-by-case basis. Fellows have been integrated into the ITHS implementation of Flight Tracker (Vanderbilt) to follow the career pathways alongside funded KL2 award recipients. Results/Anticipated Results: The key measures of success will be the rate of seminar fellows transitioning into K-level or similar career development awards and securing other subsequent funding. Preliminary data demonstrates significant collaborations between KL2 Scholars with different areas of scientific inquiry and promotion of at least half of our past KL2 Scholars into leadership positions at prestigious medical schools in the USA and Canada. We suspect that the trends evidenced by the career progression of early KL2 recipients will be expanded into newer and different translational research projects with the addition of the KL2 Fellows program. Discussion/Significance of Impact: The Seminar Fellows program presents a cost-effective way to increase the impact of an existing career development program by amplifying cross-boundary interactions to form a strong, diverse translational research workforce.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.016 |
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".