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Record W2032246521 · doi:10.4236/fns.2013.410a002

Food Perceptions among Adults and Registered Dietitians:Are They Similar?

2013· article· en· W2032246521 on OpenAlexaffabout
Kathleen Cloutier, Lyne Mongeau, Martine Pageau, Véronique Provencher

Bibliographic record

VenueFood and Nutrition Sciences · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsGovernment of QuebecUniversité de MontréalMinistry of Health and Social ServicesUniversité Laval
Fundersnot available
KeywordsPerceptionFood scienceMedicinePsychologyEnvironmental healthChemistry

Abstract

fetched live from OpenAlex

Purpose: To determine how adults and registered dietitians (RDs) perceived foods according to a frequency continuum, and to assess the differences between them. Methods: A sample of 1002 adults and 566 RDs were recruited. Participants had to associate 51 foods with a frequency continuum ("daily", "occasional" or "sometimes"). Food groups were created: 1) Canada's Food Guide's groups (CFG) (n = 22), 2) High in Fat or High in Sugar foods (HFHS) (n = 16), and 3) Meals (n = 13). Results: CFG were perceived as "daily" foods (adults = 56.8%, RDs = 94.5%), HFHS as "sometimes" foods (adults = 67.2%, RDs = 59.6%) and Meals as "occasional" foods (adults = 75.8%, RDs = 58.2%). Adults (all age groups) perceived that CFG and Meals should be eaten less frequently than RDs (18 to 64 years old). Younger adults perceived these two groups as to be eaten more frequently than older respondents. Adults perceived HFHS as to be consumed less frequently than RDs (no age effect). Conclusions: While adults tend to have more severe perceptions than RDs, results show that their food perceptions are in line with an overall awareness of Canadian nutrition guidelines, suggesting the presence of a relevant popular knowledge about the value of food.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.034
GPT teacher head0.227
Teacher spread0.194 · 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 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

Citations3
Published2013
Admission routes2
Has abstractyes

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