Sustainability‐informed dietetics education: key messages for educators and national dietetics associations
Bibliographic record
Abstract
BACKGROUND: Dietetics curricula currently fail to meet the educational needs of the future dietetic workforce to contribute expertise in sustainable, healthy food systems in the settings in which dietitians work. A 'Global Networking Event on Sustainable Food Systems in Nutrition and Dietetics Education' was held in June 2023 with the goals of building relationships among international stakeholders and informing the development of shared curricula. METHODS: Plenary lectures, panels and roundtable discussions were held over 2 days, designed to provide the background required to generate informed actions. Topics included recent research from practice and education, competency standards and relevant policy documents, examples from the field, 'big questions' about scope and student perspectives. Key messages were summarised thematically to inform educators and national dietetics associations. RESULTS: Fifty-five delegates attended from 11 nations representing education, research, dietetic associations, industry and diverse practice backgrounds. Key priorities identified for educators included co-development of curricular frameworks and pedagogical theory, practical training supports and solutions to limited time and expertise. Key recommendations for national dietetics associations included strategic promotion of sustainable food systems in dietetic roles and practical supports. CONCLUSIONS: Outcomes are anticipated to stimulate ongoing discussion, collaboration and actions on sustainable food systems education within the dietetics profession leading to shared curricular models and supports.
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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 | 0.035 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.025 | 0.019 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".