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Does High Blood Pressure Increase Perceived Healthiness of Foods with Sodium Claims on the Label?

2012· article· en· W3176476962 on OpenAlexafffundabout
Christina L. Wong, Julio Mendoza, Mary R. L’Abbé

Bibliographic record

VenueThe FASEB Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsUniversity of GuelphUniversity of Toronto
FundersAdvanced Foods and Materials Network
KeywordsCLARITYTasteHealth claims on food labelsPerceptionProduct (mathematics)PsychologySocial psychologyMedicineFood scienceChemistryBiochemistryMathematics

Abstract

fetched live from OpenAlex

This study measured Canadian consumers’ perceived clarity of the wording and the healthiness perception of food products carrying different types of claims related to sodium and examined the effect of having high blood pressure (BP) on these perceptions. Online panellists (n=803) were randomly assigned to one of four identical mock canned soup labels carrying the same Nutrition Facts table, differing only by the claim on the label: 1) nutrient content (NC), 2) function claim, 3) disease risk reduction (DRR) claim, or 4) ‘tastes great’ claim acting as a control. Overall, panellists, regardless of BP, rated the NC claim as the most clear, while the function claim was found to be the least clear (p<0.001). DRR and taste claims were intermediate. Despite the NC claim being the most clear, participants perceived the product with the DRR claim as the healthiest, and all 3 sodium claims were perceived healthier than the taste claim (p<0.001). Those with high BP (n=226) consistently rated all products with sodium related claims healthier compared to those with normal BP (p=0.02); furthermore products with sodium claims, irrespectively of the type, were perceived as healthier than the one with the taste claim. This study demonstrates that, although clarity was rated similarly irrespective of BP, having high BP increases the perceived healthiness of products carrying claims mentioning sodium. Supported by: AFMNet; CIHR STIHR Grant Funding Source : AFMNet; CIHR Strategic Training Program in Public Health Policy (CW); University of Toronto McHenry Chair (ML)

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.297
Threshold uncertainty score0.590

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.268
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2012
Admission routes3
Has abstractyes

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