Commercial food advertising on the campus of Ghana’s largest University
Bibliographic record
Abstract
Background Non-Communicable Diseases (NCDs) are a leading cause of death globally. NCD mortality attributable to unhealthy food environments (FEs) is significant. Heavy marketing of unhealthy foods is an important contributor to unhealthy FEs. Aims We examined the extent of commercial food advertising, messaging, and signage on the campus of Ghana’s oldest and largest university. Methods We cross-sectionally collected data on all sighted advertisements. Advertisements/signage were categorised as food or non-food adverts, and as healthy or unhealthy (if they were food). Results Of 503 advertisements recorded, 238 (47.3%) were food ads. Advertised food products were categorised as healthy (38.7%), unhealthy (57.6%), or other/miscellaneous (3.8%). The most advertised food product was sugar-sweetened drinks (37.0%). Different promotional techniques deployed included the use of claim pronouncement, promotional characters, emotional appeal, premium offer, and price promotion. Conclusions The preponderance of unhealthy food advertising on the campus of Ghana’s largest university has public health implications. Advertising may influence purchasing behaviour and consumption of unhealthy foods. Publicity and advocacy that motivate development of local policies to regulate various food promotion activities within this, and other Ghanaian food environments are urgently needed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".