Sustainable food systems education in nutrition and dietetics: an appraisal of the tertiary landscape in multiple countries
Bibliographic record
Abstract
Purpose Despite a growing awareness of the gap between professional expectations and competence, there has been no comprehensive appraisal of sustainable food systems (SFS) education within dietetics and nutrition programs to date. Dietitians and nutritionists play important roles in promoting sustainability yet many perceive themselves to be inadequately trained. The purpose of this study was to explore how, and to what degree, SFS education is incorporated into accredited nutrition and dietetics programs in the United Kingdom, Australia and Canada. Design/methodology/approach A content analysis of course descriptions from program websites was conducted between 2021 and 2022. Courses were reviewed, analyzed and evaluated using a novel sustainability metric. Findings SFS is integrated into the education environment of some, but not all, dietetics and nutrition programs to varying degrees (no, partial and full). Partial and full integration was present in a small percentage of courses, with a larger percentage in nutrition programs. SFS education was offered more often through a single unit than a dedicated course. Twelve best practice examples of courses dedicated to SFS were identified. In the UK, their focus was nutrition and diet, contrasting food and food systems in Australia and Canada. Originality/value These findings provide insight into SFS education for professional societies, instructors and program directors. Through intentional curricular design considerations supported by this study, program leads can take small conscious reorganizational steps to integrate SFS. This study offers a sound methodology to initiate and benchmark further assessment and a novel approach for other professions looking to equip their future workforce through SFS education.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".