Advertising of unhealthy foods and beverages around primary and junior high schools in Ghana's most urbanized and populous region
Bibliographic record
Abstract
Introduction: The advertising of energy-dense, nutrient-poor foods and beverages is a common feature in obesogenic food environments. Such advertising, within and around settings where children live, learn, and play, negatively affects their food acquisition and consumption. We examined the extent and nature of food and beverage advertising around primary and junior high schools in Ghana's most populous and urbanized region, Greater Accra. Materials and methods: Outdoor advertisements for foods and beverages within a 250 m road network distance of 200 randomly sampled schools were geocoded. For each food and beverage advertisement, information was collected on the setting, type, size, and number of product types featured in the advertisement. Promotional techniques (promotional characters and premium offers) used in advertisements were documented. Advertised foods and beverages were classified using the INFORMAS and NOVA food classification systems. Results: A total of 5,887 advertisements were identified around the schools surveyed, 42% of which were for foods and beverages. Advertisements were most prevalent at food outlets (78% of all food advertisements), but also along roads and on non-food structures. Overall, 70% of food advertisements featured non-core/unhealthy products, while 12 and 14% had core/healthy and miscellaneous (including soup cubes, seasonings, and tea) products. About 4% of food advertisements had only a product/brand name or logo displayed. One out of two of the foods and beverages advertised were ultra-processed foods, 30% processed, 3% processed culinary ingredients, and 17% unprocessed or minimally processed foods. Sugar-sweetened beverages were the most advertised food product type (32%). Promotional characters were found on 14% of all food advertisements (most-69% were cartoons or manufacturer's characters), while 8% of all food advertisements had premium offers (including price discounts and gift/collectables). Conclusions: There is an abundance of unhealthy food advertisements around primary and junior high schools in the Greater Accra Region. Policy actions such as restricting the promotion of unhealthy foods in children's settings are needed to protect pupils from such advertising practices.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".