‘Taking Sustainable Food Systems Teaching Out of the Box’: Embedding Transformative, Systems‐Engaged Pedagogy in Canadian Dietetic Education
Bibliographic record
Abstract
PURPOSE: Canadian dietetic educators are incorporating sustainable food systems (SFS) education into teaching and training as a goal towards more systems-oriented practice. This research examines SFS education in accredited dietetics programs within a Canadian post-secondary institution. The purpose is to examine how SFS is covered and to identify barriers, resource needs, and required supports among dietetic educators. METHODS: A cross-sectional, mixed-methods survey design consisting of two phases was used. A convenience sample of dietetic educators who had taught an SFS-related course within the past 5 years completed a web-based questionnaire (Phase 1) and were invited to participate in an in-depth interview (Phase 2). Quantitative data were analyzed using descriptive statistics and qualitative responses were analyzed thematically. RESULTS: Sixteen dietetic educators, representing 11 eligible institutions, participated in the study. Five participants took part in an interview. Despite widespread support for SFS, SFS education is largely limited to one course per program. Several integrated challenges concerning limited SFS coverage were identified, including a lack of coordinated, scaffolded learning, a minimum curriculum that is instructor-dependent, and the need for pedagogy that supports effective learning. Despite a clearly recognized need for support from departments, institutions and professional associations, promising examples of SFS teaching were identified. CONCLUSIONS: To equip future professionals with the knowledge and skills necessary to facilitate healthy and sustainable dietary patterns and food environments, coordinated action is needed. Program directors can signal the importance of SFS by incorporating it into the curriculum and intentionally scaffolding SFS across multiple program courses. Professional regulators can enhance the specificity of SFS within the competency standards. University administrators can lead within the institution, signalling the importance of SFS and creating opportunities for interdisciplinary teaching and learning. These findings can be used to better support programs and plan for more intentional, multi-year, transformative and systems-engaged SFS pedagogy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".