Sustainable food systems and diets in dietetic training standards: an international content analysis
Bibliographic record
Abstract
BACKGROUND: Low professional confidence and perceived competence create tangible barriers to integrating sustainable food systems (SFS) and diets into dietetic practice. One opportunity to facilitate more systemic integration into dietetic education and training is to include these concepts in professional standards. To better understand the barrier of low professional confidence and perceived competence for engagement with SFS-related practice, the purpose of this research was to investigate dietetic training standards for SFS content and to highlight opportunities for growth within the profession. Questions posed by this research are: (1) how, if at all, are SFS and diets articulated in dietetic training standards, and (2) to what level of cognitive complexity? METHODS: A content analysis of dietetic training standards documents was conducted between 15 April and 15 September 2021. Search terms included 'sustain*' or 'sustainable', 'food systems' and/or 'diets'. Extracted data with applicable SFS content were analysed for level of cognitive complexity requirements. RESULTS: Of 47 National Dietetics Associations, researchers obtained 23 dietetic training standards documents, of which 16 included SFS-related content. The majority of documents used broad descriptors of the concepts, with little granularity and at a lower level of cognitive complexity. CONCLUSIONS: Adoption of more robust frameworks for sustainability with specific learning outcomes that can be adapted to regional contexts would strengthen higher education curricula and thus the profession's ability to contribute more meaningfully to SFSs and diets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".