Street Food Environment in Maputo (STOOD Map): a Cross-Sectional Study in Mozambique
Bibliographic record
Abstract
BACKGROUND: Street food represents a cultural, social, and economic phenomenon that is typical of urbanized areas, directly linked with a more sedentary lifestyle and providing a very accessible and inexpensive source of nutrition. Food advertising may contribute to shaping consumers' preferences and has the potential to drive the supply of specific foods. OBJECTIVE: The purpose of this study is to characterize the street food offerings available to the urban population of Maputo, the capital city of Mozambique, and the billboard food advertising in the same setting. METHODS: People selling ready-to-eat foods, beverages, or snacks from venues such as carts, trucks, stands, and a variety of improvised informal setups (eg, shopping carts, trunks of cars, sides of vans, blankets on the sidewalk, etc) will be identified in the district of KaMpfumu. We will gather information about the actual food being sold through direct observation and interviews to vendors, and from the billboard advertising in the same areas. A second phase of the research entails collecting food samples to be analyzed in a specialized laboratory. The street food environment will be characterized, overall and according to socioeconomic and physical characteristics of the neighborhood, using descriptive statistics and spatial analysis. The study protocol was approved by the National Committee for Bioethics for Health in Mozambique. RESULTS: Data collection, including the identification of street food vending sites and billboard advertising, started on October 20, 2014, and lasted for 1 month. The collection of food samples took place in December 2014, and the bromatological analyses are expected to be concluded in August 2015. CONCLUSIONS: The district of KaMpfumu is the wealthiest and most urbanized in Maputo, and it is the area with the highest concentration and variety of street food vendors. The expected results may yield important information to assess the nutritional environment and the characteristics of the foods to which a great majority of the urban population living or working in Maputo are exposed. Furthermore, this study protocol provides a framework for a stepwise standardized characterization of the street food environment, comprising 3 steps with increasing complexity and demand for human and technical resources: Step 1 consists of the evaluation of food advertising in the streets; Step 2 includes the identification of street food vendors and the characterization of the products available; and Step 3 requires the collection of food samples for bromatological analyses. This structured approach to the assessment of the street food environment may enable within-country and international comparisons as well as monitoring of temporal trends.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".