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Record W1980183564 · doi:10.1017/s1368980015000658

Nutrition labelling, marketing techniques, nutrition claims and health claims on chip and biscuit packages from sixteen countries

2015· article· en· W1980183564 on OpenAlexaff
Alexandra Mayhew, Karen Lock, Roya Kelishadi, Sumathi Swaminathan, Claudia S Marcilio, Romaina Iqbal, Mahshid Dehghan, Salim Yusuf, Clara K Chow

Bibliographic record

VenuePublic Health Nutrition · 2015
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
FundersNational Institute for Health and Care Research
KeywordsNutrition facts labelLabellingHealth claims on food labelsNutrition LabelingEnvironmental healthBusinessMarketingPackaging and labelingFood marketingMedicineFood sciencePsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: Food packages were objectively assessed to explore differences in nutrition labelling, selected promotional marketing techniques and health and nutrition claims between countries, in comparison to national regulations. DESIGN: Cross-sectional. SETTING: Chip and sweet biscuit packages were collected from sixteen countries at different levels of economic development in the EPOCH (Environmental Profile of a Community's Health) study between 2008 and 2010. SUBJECTS: Seven hundred and thirty-seven food packages were systematically evaluated for nutrition labelling, selected promotional marketing techniques relevant to nutrition and health, and health and nutrition claims. We compared pack labelling in countries with labelling regulations, with voluntary regulations and no regulations. RESULTS: Overall 86 % of the packages had nutrition labels, 30 % had health or nutrition claims and 87 % displayed selected marketing techniques. On average, each package displayed two marketing techniques and one health or nutrition claim. In countries with mandatory nutrition labelling a greater proportion of packages displayed nutrition labels, had more of the seven required nutrients present, more total nutrients listed and higher readability compared with those with voluntary or no regulations. Countries with no health or nutrition claim regulations had fewer claims per package compared with countries with regulations. CONCLUSIONS: Nutrition label regulations were associated with increased prevalence and quality of nutrition labels. Health and nutrition claim regulations were unexpectedly associated with increased use of claims, suggesting that current regulations may not have the desired effect of protecting consumers. Of concern, lack of regulation was associated with increased promotional marketing techniques directed at children and misleadingly promoting broad concepts of health.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.077
GPT teacher head0.339
Teacher spread0.261 · 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 designNot applicable
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

Citations40
Published2015
Admission routes1
Has abstractyes

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