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Record W3111271975 · doi:10.12927/hcpol.2020.26349

The Ontario Public Does Not Understand the Difference Between Registered Dietitians and Unregulated "Nutritionists”: Results from a Cross-Sectional Mixed Methods Study

2020· article· en· W3111271975 on OpenAlexaffvenueabout
Deanna Veloce, Marley Fisher, Colleen O’Connor, Brenda Hartman, Justine Horne

Bibliographic record

VenueHealthcare policy · 2020
Typearticle
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsLawson Health Research InstituteWestern University
Fundersnot available
KeywordsCross-sectional studyMedicineFamily medicineEnvironmental healthNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Current Ontario healthcare policy permits anyone to use the title "nutritionist" and practice as a clinician regardless of education and training. The title "dietitian," on the other hand, is protected under the Dietetics Act (1991) for use exclusively by individuals who undergo rigorous education and training in evidence-based nutrition. OBJECTIVES: The objectives of this study were to: identify whether the Ontario general public understands the difference between a registered dietitian (RD) and an unregulated "nutritionist;" understand experiences with RDs and "nutritionists;" and determine if the current nutrition landscape arising from gaps in healthcare policy has the potential to harm the public. METHODS: A cross-sectional mixed methods survey study was carried out using inductive content analysis, descriptive statistics and chi-square tests. RESULTS: Respondents (n = 402) did not understand the difference between RDs and "nutritionists." Overall, public experiences have been significantly more positive when nutrition information/advice stemmed from an RD. IMPLICATIONS: This study provides justification for proposed legislative amendments to the Dietetics Act (1991) and the Regulated Health Professions Act (1991). These proposed amendments have been detailed in the full-text manuscript.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
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 head0.387
GPT teacher head0.508
Teacher spread0.120 · 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.

Study designObservational
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

Citations7
Published2020
Admission routes3
Has abstractyes

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