Exploring Consumer Understanding and Perceptions of Front-of-Pack Labelling of Foods and Non-Alcoholic Beverages in Kenya
Bibliographic record
Abstract
Background: Front-of-package labeling (FOPL) is shown to support healthier consumer choices. Many countries have adopted different FOPL systems. Objective: This study explored perceptions and understanding of three FOPLs and identified features that could enhance their effectiveness in Kenya. Methods: A qualitative study was conducted across four Kenyan counties—Nairobi, Mombasa, Garissa, and Kisumu. Data from 12 focus group discussions with 137 adults of diverse socio-demographic backgrounds were analysed. Participants evaluated three FOPLs: Red and Green (RG) Octagon, Red and Green Octagon with icons and text (RGI), and Black Octagon Warning Label (WL). The FGDs assessed visibility and memorability, comprehension, potential effectiveness, and cultural relevance of each label. NVivo version 14.0 was used for thematic analysis. Results: Kenyan consumers had mixed perceptions of the proposed FOPLs. The black Octagon WL was found to be the most visible and memorable due to its bright colours. Although the RG and RGI symbols were visually engaging, some participants reported confusion with the colour meanings. The WL was also more readily understood due to its text. Overall, WL was preferred for its potential to influence purchasing decisions, while all three FOPLs were considered culturally suitable. Conclusions: The Black Octagon Warning Label was the most visible and comprehensible of the three FOPLs and shows promise in influencing consumer behaviour in Kenya. While RG and RGI symbols are appealing, their colour scheme could reduce their effectiveness. Educating consumers on FOPLs could enhance their impact in reducing unhealthy food purchases.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".