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Record W6906513975 · doi:10.17605/osf.io/6wvea

Identifying and Mapping Perceptions, Experiences, and Knowledge of Canadian Registered Dietitians with Weight-Related Evidence in Nutrition Care: A Scoping Review

2024· article· en· W6906513975 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2024
Typearticle
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsCINAHLScopusMEDLINEGrey literatureFoundation (evidence)Delphi methodEvidence-based medicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.175
GPT teacher head0.481
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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