BRUSH Summer Research Program: Promoting Science Identity in Underrepresented Veterinary and Undergraduate Students
Bibliographic record
Abstract
While all facets of the health care workforce need to diversify, the veterinary profession lags behind in training students from underrepresented populations. The need to increase diversity among health care professionals is not limited to clinicians but extends to those generating new information through biomedical research. To address demographic disparities within the biomedical research community, we provide a summer research program for veterinary and undergraduate students from populations historically underrepresented in the biomedical workforce that is explicitly designed to foster science identity and subsequently increase participants’ interest and success in pursuing biomedical research-related educational and career paths. We hypothesized that participation in this program would enhance science identity, confidence, and pursuit of research-related education and subsequent careers. Three validated survey instruments containing qualitative ordered rating scales were administered to program participants ( N = 57) over the course of the summer in which they participated (2018–2022). Questions asked at two time points were analyzed with a repeated-measures linear mixed-effects model. Significant growth was reported in most topics surveyed over time. Many queries within gains, confidence, and science identity modules displayed significant increases over time or scored high in surveys at both time points. In addition, post-graduate educational and career outcomes were obtained for alumni ( N = 130) of program years 2011–2023; their post-graduate enrollment rates (78%) markedly exceeded national norms. This multidimensional experiential research program, which holistically fosters professional networking and student confidence in research-related endeavors, provides quantifiable growth in research skills and science identity. These gains support students’ persistence in research and biomedical-related educational and career paths.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".