A social media intervention for dietetics professionals to increase awareness about racial/ethnic diversity and inclusion in dietetics: Black voices centered
Bibliographic record
Abstract
The Academy of Nutrition and Dietetics (the Academy) is a professional organization founded by and largely for white women. Black-identifying dietetics professionals currently make up only 2.6% of credentialed professionals, while Black-identifying residents comprise 13.4% of the US population. To understand participant opinions, beliefs, experiences, knowledge, and actions related to racial ethnic diversity and inclusion (REDI) in general and in dietetics specifically we conducted a 20-week intervention study, delivered over a social media platform (Facebook group). The content, developed prior to the intervention, was informed by the Trans-theoretical Model of Change and Critical Race Theory and was structured to provide educational content related to REDI. Participants completed baseline, and then a follow up survey after the 20-week intervention. Here we present baseline data from (n=30) Black-identifying participants of the main study. Participants were mostly young, female, Academy member RDNs with at least a Master’s degree. They voiced strong opinion that dietetics is neither diverse nor inclusive, and that the Academy should actively engage in efforts to enhance diversity in the profession. They believe that the Academy should focus on REDI and that it is important that white-identifying members engage in that work. Participants reported engaging in conversations and with media about race/privilege in their personal and professional lives, and that they had either experienced or witnessed microaggression while performing their jobs in dietetics. Results of this sub-study offer insight into the Black experience in dietetics as well as ways the Academy can improve diversity and inclusion within its organization and membership.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".