Urban school neighbourhoods dominated by unhealthy food retailers and advertisements in Greater Tunis: a geospatial study in the midst of the nutrition transition
Bibliographic record
Abstract
Abstract Objective: Food environments are a major determinant of children’s nutritional status. Scarce evidence on food environments exists in low- and middle-income countries (LMIC). This study aims to fill this gap by documenting the obesogenicity of food environments around schools in Greater Tunis, Tunisia – an LMIC of the Middle East and North Africa region with an ongoing nutrition transition and increasing rates of childhood obesity. Design: In this cross-sectional study, we assessed built food environments around fifty primary schools. Ground-truthing was performed to collect geographic coordinates and pictures of food retailers and food advertisement sets within an 800-m road network buffer of each school. Retailers and advertisement sets were categorised as healthy or unhealthy according to a NOVA-based classification. Associations between school characteristics and retailers or advertisement sets were explored using multinomial regression models. Setting: Greater Tunis, Tunisia. Participants: Random sample of fifty (thirty-five public and fifteen private) primary schools. Results: Overall, 3621 food retailers and 2098 advertisement sets were mapped. About two-thirds of retailers and advertisement sets were labelled as unhealthy. Most retailers were traditional corner stores (22 %) and only 6 % were fruit and vegetable markets. The prevailing food group promoted was carbonated and sugar-sweetened beverages (22 %). The proportion of unhealthy retailers was significantly higher in the richest v. poorest areas. Conclusions: School neighbourhood food environments included predominantly unhealthy retailers and advertisements. Mapping of LMIC food environments is crucial to document the impact of the nutrition transition on children’s nutritional status. This will inform policies and interventions to curb the emergent childhood obesity epidemic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".