Strategies for Restructuring Dietetics Education Programs to Improve Nutrition Equity in Indigenous Populations: A Narrative Review
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Particularly in racially and ethnically diverse countries, the necessity of providing individualized care to people seeking diet advice is increasingly recognized and embedded in practice guidelines. Some jurisdictions have a history of colonization with subjugation and marginalization of the Indigenous population, which has led to serious health inequities. One overarching strategy to reduce health inequities is to provide education through a decolonizing lens, so that graduate healthcare professionals, such as dietitians, have a better understanding of how to mitigate colonial attitudes, racism, stereotyping and other behaviours, thereby improving health equity. This review aims to summarize and evaluate educational strategies to decolonize dietetics training programs. METHODS: A narrative review was conducted. RESULTS: Professional dietetics organizations in Canada, Australia and New Zealand have incorporated Indigenous-specific outcomes into their standards of practice. Six primary research studies were reviewed, two each from Australia, New Zealand and the United States. The strategies developed include reviewing curriculum content, providing experiential learning opportunities and identifying barriers to the participation of Indigenous students in dietetics programs. Lack of engagement of Indigenous persons in curriculum development, planning and evaluation of efforts is a gap that needs to be addressed. CONCLUSIONS: Meeting practice standards and closing the health equity gap for Indigenous peoples require additional research and implementation into practice.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".