MétaCan
Menu
Back to cohort
Record W4205816139 · doi:10.3148/cjdpr-2021-033

Towards a Standardized Definition of Medical Nutrition Therapy and Regulatory Reform in Canada

2022· article· en· W4205816139 on OpenAlexafffundvenueabout
Justine Keathley, Amélie Arbour, Marie‐Claude Vohl

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsMisinformationHarmLegislatureMedicinePublic healthMedical adviceBest practiceHealth carePublic relationsNursingPolitical science

Abstract

fetched live from OpenAlex

Various definitions have been proposed to describe Medical Nutrition Therapy (MNT). Broadly, MNT encompasses the provision of nutrition information and advice aimed to prevent, treat, and/or manage health conditions. In Canada, the provision of such information and advice is unregulated, thus allowing anyone to provide MNT services regardless of their education and training. This inevitably poses risks of harm such as the provision of unsafe and/or ineffective nutrition advice as well as delayed evidence-based treatment. Canadian research has further demonstrated that the general public is unable to properly differentiate between regulated, evidence-based nutrition providers (registered dietitians) and those who are unregulated. Therefore, the public is at risk. To reduce nutrition misinformation and ultimately improve the health and well-being of the public, the objective of this paper is, first, to propose a standardized definition of MNT for use across Canada and, second, to propose province- and territory-specific legislative amendments for the regulation of MNT throughout the country. We also present an opposing perspective to the proposed viewpoint. Ultimately, health care regulation across the country requires an overhaul before we expect that nutrition information and advice communicated to the public may be consistently evidence based.

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)0.008
metaresearch head score (Gemma)0.002
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 score0.479
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.174
GPT teacher head0.460
Teacher spread0.286 · 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

Citations8
Published2022
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Journal of Dietetic Practice and ResearchSame topicDietetics, Nutrition, and EducationFrench-language works237,207