Sustainability and Food Systems Concepts in Dietetic Training Standards in Speaking Spanish Countries
Bibliographic record
Abstract
Introduction: Global calls for action to support sustainable development through food systems and nutrition provide context to examine to what degree nutrition and dietetics professionals are equipped for this challenge. The purpose of this research is to investigate content related to sustainable food systems in training standards from Spanish-speaking countries and examine what level of knowledge is required. Methods: Researchers conducted a content analysis of documents informing nutrition and dietetics training standards for content related to sustainable food systems, including dimensions of these complex topics. Relevant content was then analyzed according to the level of cognitive complexity per Bloom's Revised Taxonomy. Results: Of 21 eligible countries, documents describing competencies, standards or codes of ethics were found for six, four of which included relevant standards: Colombia, Mexico, Paraguay, and Peru. Overall, there was minimal comprehensive inclusion of sustainable food systems, but partial inclusion of one or more important sustainability dimensions. These were required at a mix of levels of cognitive complexity. Conclusions: This research adds to a small body of evidence documenting the state of readiness of nutrition and dietetics professionals to contribute to sustainable development. It highlights a moderate level of readiness in four Spanish-speaking countries, and opportunities for increased emphasis on comprehensive sustainability-informed education and training standards, which can help prepare practitioners for effective practice. Funding: MITACS Global Research Internship.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".