Blacks and racism in the dietetics field: From diet-related health disparities to racial microaggressions
Bibliographic record
Abstract
The impact of racism on the field of nutrition and dietetics is pervasive and manifests itself in a variety of ways.The national context of police brutality against Blacks has shed light on other aspects of structural and systemic racism in this country.These include inequities in our education system, income inequality, and affordability and quality of housing.Those directly relevant to the field of nutrition and dietetics are disparities in access to affordable and healthy foods and issues of food insecurity.Related to these inequities, African Americans are disproportionately impacted by diet-related diseases, such as heart disease, hypertension, and diabetes (Cunningham et al., 2017).Lack of diversity in the field of nutrition and dietetics is also arguably an artifact of systemic racism in this country.African Americans/Blacks represent only 2.6% of the dietitians in the U.S and between 1998-2016, the percentage of African American dietetic students declined by 11.6% (Burt et al., 2019).Rooted in systemic racism, much of the decline of African American dietitians stems from the closing of nutrition and dietetics program at Historically Black Colleges and Universities (HBCUs) and declines in funding for HBCUs since the 1970s, leaving publicly funded HBCUs at the mercy of these policy changes.Several of these programs transitioned to hospitality programs and restaurant management, ultimately shutting off African American students to the dietetics profession.The financial cost of the 10-12 month unpaid dietetic internship is also a barrier to entry in the field for Black students.As a result of the lack of diversity in the field, race-based microaggressions are also commonplace in the dietetics profession.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".