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Record W4396970788 · doi:10.1177/21582440241241982

Consumer Awareness, Knowledge, Understanding, and Use of Nutrition Labels in Africa: A Systematic Narrative Review

2024· article· en· W4396970788 on OpenAlexaboutno aff
Prince Kwabena Osei, Christabel Ampong Domfe, Alex Kojo Anderson

Bibliographic record

VenueSAGE Open · 2024
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativePsychologyNarrative reviewSociologyLinguistics

Abstract

fetched live from OpenAlex

The purpose of nutrition information on a nutrition label is to communicate to consumers the nutritional content of prepackaged foods so that they would be able to identify healthy foods before purchase. Many systematic reviews in the area of consumer awareness, knowledge, understanding, and use of nutrition labels have focused on the United States, Canada, Asia, Europe, Australia, and New Zealand, and little attention has been given to African countries. To review the state of consumer awareness, knowledge, understanding, and use of nutrition labels within the African region, identify barriers to the use of nutrition labels, identify consumers who are more likely to use labels, and assess the factors that affect purchasing decisions. Searches were done in electronic databases (PubMed, Google Scholar, Semantic Scholar, Web of Science) and the reference lists of relevant research articles (back referencing). The review was limited to cross-sectional peer-reviewed research articles which were published in the English Language between January 2000 and June 2022. Twenty-six peer-reviewed papers from 10 African countries that met our inclusion criteria are included in this systematic review. The overall crude means of levels of awareness, knowledge, understanding, and use of nutrition labels were found to be 74.2%, 56.4%, 45.3%, and 69.1%, respectively. Consumer levels of knowledge and understanding of nutrition labels across the 10 African countries were low compared to the awareness and use of nutrition labels.

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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.143
GPT teacher head0.376
Teacher spread0.234 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2024
Admission routes1
Has abstractyes

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