Bibliographic record
Abstract
The dietetics profession is comprised of practitioners who understand the relationship between food and health. Registered Dietitians (RDs) can work in a variety of fields ranging from acute care hospitals, food service institutions, community settings, and research or marketing arenas. A wide variety of populations are served by RDs, including minority populations. Several minority populations such as African Americans and Hispanics tend to be at higher nutritional risk, and often have higher rates of obesity and chronic diseases, such as diabetes, hypertension, and cardiovascular disease. Despite the fact that these disparities tend to be related to nutrition, the RDs who serve these populations tend to be primarily white and female (96%). This problem has been addressed within the profession for decades and little progress has been made. The success of white RDs treating non-white patients has been questioned and could lead to lower quality of care. The purpose of this study was to investigate the perceptions and experiences of minority dietitians throughout their education. Barriers that prohibit minorities from entering the field were also be explored. To explore this phenomenon, minority female dietitians were interviewed to investigate their perceptions of the field and their higher education experience. This work answers the question: What are the experiences of minority female professional dietitians regarding training, education, and practice? Using Critical Race Theory (CRT) as the lens, results showed that minorities have had disgraceful treatment in their higher education and internship experiences and have had to work harder than their white peers.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".