National Institutes of Health diversity supplements: Perspectives from administrative insiders
Bibliographic record
Abstract
BACKGROUND: The NIH Diversity Administrative Supplement is a funding mechanism that provides support for diverse early-stage researchers. There is limited guidance on how to apply for these awards. PURPOSE: We describe perspectives of NIH program/diversity officers and university research administrators offering recommendations for diversity supplement submission. METHODS: This article is the product of a working group exploring diversity in research. Nursing faculty from an R2 Historically Black College and University and an R1 research intensive university conducted stakeholder interviews with NIH program/diversity officers and university research administrators. We used content analysis to categorize respondents' recommendations. FINDINGS: Recommendations centered on harmonizing the applicant with the program announcement, communication with program/diversity officers, mentor/mentee relationship, scientific plan, and systematic institutional approaches to the diversity supplement. DISCUSSION: Successful strategies in submitting diversity supplements will facilitate inclusion of diverse researchers in NIH-sponsored programs. Systematic approaches are needed to support development of diverse voices to enhance the scientific community.
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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 | 0.124 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.042 | 0.013 |
| Scholarly communication | 0.024 | 0.009 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".