Diversity demographics are needed to enhance accurate assessment of diversity in the nutrition and dietetics profession
Bibliographic record
Abstract
A diverse workforce is vitally important to ensure that our nation has quality, affordable, and accessible health care. Diversity, equity, and inclusion are crucial to the nutrition/dietetics profession. As the Academy of Nutrition and Dietetics (Academy) strives to increase and better target its efforts in these areas, assessment will be required to measure progress. Evaluation of progress hinges on the cooperation of individual nutrition/dietetics practitioners — registered dietitians (RDs) or registered dietitian nutritionists (RDNs) and dietetic technicians registered (DTRs) and nutrition dietetics technicians, registered (NDTRs) to report race/ethnicity and/or gender to the Academy and/or the Commission on Dietetic Registration (CDR). Furthermore, accurate assessment and reporting of diversity demographics of the profession rely on the Academy and/or the CDR to use the full de-identified database of practitioners rather than smaller surveys. This article 1) summarizes reports on practitioners in health occupations by race/ethnicity and gender diversity from the CDR, U.S. government, and other professional organizations, 2) summarizes data collection standards issued by the U.S. government on race and ethnicity, 3) provides a recommended action step to encourage nutrition/dietetics practitioners to self-report race/ethnicity and gender to the Academy and/or the CDR, and 4) provides recommended action steps to encourage the Academy and/or the CDR to use the full de-identified database of nutrition/dietetics practitioners rather than smaller surveys to report diversity demographics of RDs/RDNs and DTRs/NDTRs by Academy groups, and to make several revisions to the CDR Registry Statistics and surveys to enhance future diversity and more accurate assessment of diversity in the profession.
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 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 | 0.029 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".