How Can Dietitians Leverage Change for Sustainable Food Systems in Canada?
Bibliographic record
Abstract
Purpose: In this paper, we begin to set out language defining sustainable food systems (SFS) in Canada, through the voices of dietitians, and identify leverage points where dietitians can affect change. Methods: Dietitians of Canada members were invited to a Delphi Inquiry process; questions explored a vision of SFS in Canada, barriers to that vision, and actions. Results were independently analysed by 2 members of the research team who used the Framework for Strategic Sustainable Development to structure the data. Results: Fifty-eight members participated. The resultant vision describes a future food system in 15 thematic areas of the social and ecological systems. Barriers are described according to how they undermine sustainability. High-leverage actions areas included: (i) facilitating knowledge development within the profession and public, (ii) influencing organizational policy to support SFS, and (iii) and influencing public policy. Approaches to such action included: (i) facilitating cross-sectoral collaboration and (ii) applying reflexive approaches. Conclusions: This research suggests a multidimensional understanding of food systems sustainability among dietitians. The vision provides some language to describe what dietitians mean by SFS and can be used as a compass point to orient action. Action areas and approaches have the potential to drive systemic change while avoiding unintended consequences.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".