Identifying and Mapping Canadian Registered Dietitians’ Perceptions and Knowledge of, and Experiences with, Weight-Related Evidence in Nutrition Care: A Scoping Review Protocol
Bibliographic record
Abstract
Objective: The objective of this scoping review is to identify and map the currently available peer-reviewed and grey literature exploring Canadian registered dietitians’ (RDs’) perceptions and knowledge of, and experiences with, weight-related evidence in nutrition care. Introduction: Weight, skin fold calipers, body mass index (BMI), and other means of measuring and describing body size, have been associated with risk, progression, and nutrition intervention success with several disease states. Interpretation and application of weight-related evidence can be impacted by several non-medical factors, including practitioner perspective, evidence interpretation and application, lived experience, and bias. Each of these outcomes may differ between RDs and are not easily described or understood. Inclusion Criteria: Original peer-reviewed studies and grey literature published in English that explore Canadian RDs’ perceptions of, knowledge of, and experiences with weight-related evidence in nutrition care will be included. Methods: Following the JBI scoping review design and associated methodology, including the three-step search strategy process, four databases will be searched: CINAHL (EBSCO), MEDLINE (Ovid), Embase (Elsevier), and Scopus (Elsevier). Grey literature will be searched using Google Scholar, Google, and Microsoft Bing, and a search strategy specific to grey literature has been developed in partnership with the research team’s librarian (MR). Screening and extraction will be led by two independent reviewers (RW, AM), and conflicts will be resolved either by discussion or through a third reviewer (SG). Data will be presented using diagrams and/or tables, including a narrative summary. The Delphi method will be used for community consultation, that will occur throughout this study.
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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 | 0.165 | 0.148 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.039 | 0.030 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.008 |
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".