Street food research worldwide: a scoping review
Bibliographic record
Abstract
BACKGROUND: Street foods vary with respect to their nutritional value and safety characteristics and contribute to a sizable proportion of food intake in many populations worldwide. Therefore, the present study aimed to describe the coverage in the scientific literature of different health-related and socio-economic aspects of street food consumption and trading. METHODS: Three electronic databases (searched from inception to 16 October 2017), a hand-search of relevant journals and backward citation tracking were used to identify eligible scientific articles with a main objective of investigating or reporting specific results on health-related or socio-economic aspects of street food. Papers published in English, Portuguese, French, Spanish or Italian, as well as English abstracts of papers published in other languages, were assessed. The selected articles were evaluated by two independent researchers and described according to year of publication, geographical distribution, definition of street food, main topics addressed and target population. RESULTS: In total, 441 papers were selected. The number of publications has increased in recent years, almost half of them being published after 2012. Almost three-quarter of the articles were from Africa or Asia. Most studies addressed food safety (85.5%), whereas street food availability and consumption were much less frequently investigated (30.3%). The focus of the studies was usually the food (mostly its microbiological contamination) and the vendors (mostly their food handling), whereas consumers and vending sites were seldom evaluated. More than half of the studies did not specify a definition for street food. CONCLUSIONS: Efforts are needed for a more widespread and comprehensive assessment of different issues related to street food availability and consumption in different settings, especially regarding street food offer, nutritional composition, and patterns of purchase and consumption by the population.
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.043 | 0.036 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".