Approaches for Measuring Inclusive Demographics Across Youth Enjoy Science Cancer Research Training Programs
Bibliographic record
Abstract
The National Cancer Institute's (NCI) Youth Enjoy Science Program (YES) funds initiatives to support the cancer research training and career ambitions of middle school through undergraduate students from populations underrepresented in the biomedical sciences. The program has funded 16 institutions nationally as of January 2022. Given the program's focus on increasing diversity within the cancer research workforce, demographic characteristics of YES trainees provide essential information about the populations being served and program effectiveness. Six programs formed an interest group focused on trainee demographics and surveyed all YES grantees about their demographic data practices. Fifteen programs (94%) completed the survey. Survey data were analyzed through descriptive statistics and thematic coding. Findings revealed considerable variability in programs' approach to demographic data, including which demographics were measured, how they were operationalized, and when and how the data were collected. Half of YES programs (53%) could report underrepresented populations in biomedical research among trainees using consistent definitions. Most programs described efforts to improve their demographic data practices; however, challenges remained for the vast majority. In consideration of these findings, we offer recommendations for inclusive demographic data practices to better define and retain underrepresented populations in biomedical sciences.
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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 | 0.053 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".