Learning Through Doing: Comprehensive Programming for a Training Program in Cancer Disparities
Bibliographic record
Abstract
In the United States, preparing researchers and practitioners for careers in cancer requires multiple components for success. In this reflection article, we discuss our approach to designing a comprehensive research training program in cancer disparities. We focused on elements that provide students and early career scientists a deep understanding of disparities through first-hand experiences and skills training necessary to build a research career in the area. Our Educational Program sits within the framework of an NCI P20 program, "UHAND (University of Houston/MD Anderson Cancer Center)", jointly established by an NCI-designated comprehensive cancer center and a minority-serving university as a collaborative partnership devoted to the elimination of cancer inequities among disproportionately affected racial and ethnic groups (UHAND Program to Reduce Cancer Disparities; NCI P20CA221696/ P20CA221697). The Education Program was designed to build on and enhance skills that are critical to pursuing a career in cancer disparities research at the undergraduate, doctoral, and post-doctoral levels-such as scientific communication, career planning and development, professional and community-based collaboration, and resilience in addition to solid scientific training. As such, our program integrates (1) opportunities for learning through service to community organizations providing resources to populations with documented cancer disparities, (2) a tailored curriculum of learning activities with program leadership and mentored research with scientists focused on cancer disparities and cancer prevention, (3) professional development training critical to career success in disparities research, and (4) support to address unique challenges faced by trainees from backgrounds that are historically underrepresented in research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".