Cultural Dominance in Dietetics; Hearing the Voices, African American Nutrition Educators Speak
Bibliographic record
Abstract
Currently less than 4% of Registered Dietitians in the U.S. come from African American or Latino communities. People are often unwilling to reveal their lifestyle patterns to those they fear will be insensitive to their sociopolitical position in society. The 2002 Institute of Medicine Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health Care recommended an increase of underrepresented minorities in the health care workforce. This study analyzed the impact of white dominance in the Field of Dietetics. Nineteen African American women who practice nutrition education in the African American community were interviewed regarding their own educational experiences, their practice, and their perception of the profession of Dietetics. They were also asked to give their opinions regarding changes that need to take place in the field. Critical Race Theory was utilized as a lens to analyze the findings. The women reported racism in their educational histories. The participants commented on their ability to relate to the food, economic conditions and learning styles of their African American clients. They identified a number of obstacles to becoming Registered Dietitians, many of which centered on accessing the internship process and the lack of perceived cost/benefit. The participants discussed needed changes in the educational process, including more multicultural education for dietetic students. It is hoped that this study will provide a voice from those who have been marginalized in Dietetics to project insight on how the field might become more inclusive and effective in communities of color.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".