Consumer Awareness, Knowledge, Understanding, and Use of Nutrition Labels in Africa: A Systematic Narrative Review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".