A Bridge to Diversity in Nutrition Training
Bibliographic record
Abstract
Data from the Academy of Nutrition and Dietetics indicates little diversity amongst registered dietitians and students training in nutrition currently and historically. The new national strategy and federal agency priorities recommend diversification of the nutrition workforce as a means of strengthening the practice to meet the needs of a more diverse nation – necessary to address disparities in nutrition and nutrition-related diseases. Importantly, innovation in research results from teams that are diverse, reinforcing the necessity of diversity in the nutrition workforce. We conducted Bridging the Gap 2, an intensive nutrition research program, to recruit undergraduates from groups underrepresented in nutrition and higher education. The 10-week program provided hands-on research experience with faculty mentors, professional development, and exposure to different areas of nutrition and food-related careers. Participants received a complimentary meal plan and housing on the campus of a 4-year university. The pilot cohort (n=6) included mostly women and half identified as Hispanic/Latino. Exit survey data indicated that the majority were somewhat or extremely satisfied with the program. Qualitative responses highlighted areas for improvement including support for transportation costs, a desire for more nutrition education, and more structured daily research activities. At the conclusion of the program 50% of students who were not nutrition majors initially, indicated that they will switch to the major. The pilot nutrition research program generated positive results that indicate the ability of targeted programming to engage those underrepresented in or not studying nutrition and may be important to diversify the nutrition workforce.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".