Sustainability in Canadian Dietetic Practice
Bibliographic record
Abstract
Purpose: Dietitians (RDs) are well-positioned to drive food system transformation by supporting dietary patterns sourced from sustainable food systems (SFS). This research aims to identify how RDs conceptualize sustainability, describe SFS activities, define success, and determine the knowledge and skills required to practice in this area. Methods: A convenience sample of Canadian RDs completed a cross-sectional survey with open- and close-ended questions. Quantitative data were analyzed using descriptive statistics. Qualitative responses were thematically analyzed. Practice activities were mapped using the Socioecological Framework (SEF). Results: A diverse sample (n = 92) reported using common SFS definitions, frameworks, or other documents. Practice activities were reported on all levels of the SEF. Dietitians reported successes; however, the vagueness or responses suggested it may be too early to quantify these. Dietitians reported needing foundational and practice area-specific knowledge and skills and practical examples to support SFS in practice. Conclusions: Canadian RDs in this study demonstrated significant work in SFS using skills they developed to practice in other areas of dietetics. There is an opportunity to expand impact by sharing existing resources, developing new supports that include Indigenous perspectives and systems thinking, evolving RD roles, increasing macro-level strategies, and identifying success indicators to monitor impact.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".