Registered dietitians' perceptions and use of the plant‐based recommendations in the 2019 Canada's Food Guide
Bibliographic record
Abstract
BACKGROUND: In 2019, Canada's Food Guide underwent its first transformation in over a decade. The change received considerable attention given the increased focus on plant-based proteins and the decision to eliminate the milk and alternatives food group. Yet little is known about health professionals' views of these updates. In response, the present study examined Canadian dietitians' attitudes and behaviours towards the guide's expanded plant-based recommendations. METHODS: A pan-Canadian cross-sectional online survey was administered from January to March 2020 to currently practicing registered dietitians. The study was pre-registered, piloted and received ethical approval. Descriptive and inferential statistics were performed and open-text responses underwent thematic analysis. RESULTS: In total, 411 dietitians from 10 provinces and various work settings participated. Most dietitians (82.8%) consider the food guide's recommendation to choose protein foods that come from plants more often is evidence-based. Compared to its predecessor, dietitians encourage their patients/clients to choose protein foods that come from plants (p < 0.001) and non-dairy sources of calcium (p < 0.001) significantly more frequently under the new guide. A slight majority of dietitians (57.7%) agree with the decision to omit the standalone milk and alternatives group in favour of bringing dairy into the protein category. CONCLUSIONS: Canadian dietitians generally look favourably upon the new plant-based recommendations and have adjusted their nutrition counselling in response. The findings are considered to be the first to characterise how dietitians view changes to the plant-based content of one of the most recognisable diet-related educational tools in Canada.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".