“Some are healthy and others not”: Characterization of vended food products by Accra-based food retailers
Bibliographic record
Abstract
Background and objectives: Increasing the availability of healthy foods within food retail outlets can improve consumers' food environments. Such actions or inactions by food retailers may affect people's food purchasing and consumption behavior. This study explored Accra-based food retailers' perceptions and appreciation of "healthiness of food" as a concept. It also documented measures that food retailers adopt to encourage healthy food choices. Methods: = 13) in March 2021. The interviews were recorded, transcribed, coded, and analyzed thematically. Results: The retailers' understanding of healthy food, or lack thereof, is exemplified by such expressions as "health, absence of disease, longevity, balanced diet, diversity, sanitation, and certification." A handful of retailers described what they sell as "products that meet consumer needs," "harmless," or "generally good." Very few retailers described the food they sell as "junk," high in sugar, fat, and salt, or energy-dense but nutrient poor foods, or as food that could pose some health risk to consumers. However, some retailers indicated that they advise their customers against the overconsumption of some foods. Conclusion: Overall, Accra-based retailers have a fair understanding of what constitutes healthy food - exhibiting limited knowledge of the connection between very salty, very sugary, and very fatty foods and health outcomes. Retailers in Accra require interventions that improve their food, health, and nutrition literacy. Improving retailers' food and nutrition literacy may improve the availability of healthier options in food retail outlets in Accra.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".