Identifying and Mapping Perceptions, Experiences, and Knowledge of Canadian Registered Dietitians with Weight-Related Evidence in Nutrition Care: A Scoping Review
Bibliographic record
Abstract
This dietitian-led scoping review identified and mapped peer-reviewed and gray literature, sharing Canadian Registered Dietitians’ (RDs’) perceptions of, experiences with, and/or knowledge of weight-related evidence in nutrition care. Implementing JBI scoping review methodology, four databases were searched: 1) CINAHL (EBSCO); 2) Medline (Ovid); 3) Embase (Elsevier); and 4) Scopus (Elsevier). Reference linking was also conducted. Google and Bing were searched for gray literature. Three JBI-trained independent reviewers completed screening to extraction. Conflicts were resolved by the senior/corresponding author and co-principal investigator. Community consultation was conducted using the Delphi Method. Of 2217 results, 67 were included in the review (29 peer-reviewed; 38 gray). Identified frequencies were 67 examples of perception, 54 of experience, and 51 of knowledge. Weight-related evidence was identified in nutrition care in various settings, including research and practice, representing nutrition assessment, diagnoses, interventions, monitoring, and evaluation. These findings serve as a foundation for a global/international review and provide details on Canadian context. This work also provides a foundation for effective evaluation of dietitian-led intervention fidelity, utility, and effectiveness, using systematic review or other research designs. Lastly, this review identified diverse definitions/ perspectives; highlighting the benefits of continuing to discuss and explore this topic within and beyond dietetics.
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 | 0.082 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.065 | 0.074 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".