Individual plus Policy, System, Environment Framework to Address Racial and Ethnic Diversity, Equity, and Inclusion as an Adaptive Challenge in Dietetics
Bibliographic record
Abstract
Addressing the lack of diversity within the nutrition and dietetics profession has become a vital key to mitigating health disparities. Research has shown greater racial/ethnic diversity to be efficacious in addressing these challenges due to improved racial concordance and impacts on unconscious bias. Despite efforts to increase diversity within the dietetics profession, it continues to lack proportional representation of Black, indigenous, and people of color (BIPOC). Furthermore, it lags behind other health professionals in addressing this dearth of representation. The stagnation in outcomes from the efforts of the Academy presents a unique opportunity to examine those efforts and develop a framework for action which will cultivate synergistic strategies to impact inclusion, diversity, and equity. The Individual plus Policy, System, Environment (I+PSE) conceptual framework for action provides a blueprint for dietetics practitioners to develop and implement multidimensional strategies using a systems approach to address adaptive challenges and achieve collective impact more effectively. The diversification of the dietetics profession presents an adaptive challenge which a systematic approach would prove beneficial. The I+PSE framework was used to conceptualize the complex challenges of recruiting and retaining BIPOC dietetic professionals, specifically focusing on barriers within the pipeline, practice, and the profession. The examination of each framework construct provides a more targeted multidimensional approach by identifying key barriers and facilitators. The I+PSE framework for action provides the Academy with a means by which to critically examine root causes that are impeding initiatives to cultivate an inclusive and equitable 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 | 0.021 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".