Identifying and Mapping Canadian Registered Dietitians’ Perceptions and Knowledge of and Experiences with Weight-Related Evidence: A Scoping Review
Bibliographic record
Abstract
In this scoping review, “weight-related evidence” is an umbrella for various terms, phrases, and ways in which weight, body size, fatness, and/or obesity present in research and dietetic practice. Canadian Registered Dietitians’ perceptions of, experiences with, and/or knowledge of weight-related evidence in nutrition care was identified and mapped. Implementing JBI scoping review methodology, four databases were searched: (i) CINAHL (EBSCO); (ii) Medline (Ovid); (iii) Embase (Elsevier); and (iv) Scopus (Elsevier). Google and Bing were searched for grey literature. Three JBI-trained independent reviewers completed screening to extraction. Community consultation was conducted using the Delphi Method. Of 2217 results, 67 were included in the review (29 peer-reviewed; 38 grey). Identified frequencies were 67 examples of perception, 54 of experience, and 51 of knowledge. This review identified diverse definitions/perspectives of weight-related evidence, highlighting the benefits of continuing to discuss and explore this topic within and beyond dietetics. Weight-related evidence was identified in nutrition care in various settings, representing nutrition assessment, diagnoses, interventions, monitoring, and evaluation. Focused on dietetic research and practice, this work provides a foundation for future evaluation of dietitian-led intervention fidelity, utility, and effectiveness, using systematic review or other research designs. These Canadian findings can serve as a foundation for a global/international review.
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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 | 0.087 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.081 | 0.123 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".