Bibliographic record
Abstract
The nutrition and dietetics profession is neither diverse nor inclusive of Black, Indigenous, and People of Color (BIPOC), yet to achieve racial/ethnic diversity, equity, and inclusion (R-DEI), nutrition professionals (educators, preceptors, and professionals) need to understand and care about related issues. However, both a space and curriculum to educate professionals about R-DEI are lacking. Our aim was to understand how knowledge, actions, identity, and experiences are related to beliefs about R-DEI among nutrition professionals. We developed a 20-week curriculum about R-DEI topics using the Transtheoretical Model (Stages of Change) and Critical Race Theory. It was delivered in #InclusiveDietetics, a Facebook group, between January and August 2020. Pre- and post-intervention surveys were used to understand participants’ identities, experiences, knowledge, beliefs, actions, and opinions and analyzed using logistical regression, t-tests, and descriptive statistics. Knowledge (p<.05), experiences with racism (p<.01), and being a former Academy of Nutrition and Dietetics member (p<.05) were positively correlated with participants’ beliefs about R-DEI in dietetics (that is, more highly valued R-DEI) while actions were negatively correlated (p<.001). Significant increases in knowledge (p<.01) and beliefs aligned with R-DEI values (p<.01) were observed following the intervention, driven by increases in white participants. A significant increase in opinions aligned with R-DEI values was observed among BIPOC (p<.01), but not white participants following the intervention. The importance of a space for professionals to examine R-DEI is critical to achieve professional equity and the #InclusiveDietetics curriculum may be an effective tool to better align nutrition professionals with R-DEI values.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.465 | 0.162 |
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".