Food systems determinants of nutritional health and wellbeing in urban informal settlements: A scoping review in LMICs
Bibliographic record
Abstract
INTRODUCTION: Increasing food and nutritional inequities are apparent in urban settings across Low- and Middle-Income Countries (LMICs), along with nutrition transition towards ultra-processed diets high in fat, sugar, and salt. In urban informal settlements, characterized by insecurity and inadequate housing and infrastructure, food systems dynamics and their nutritional implications are poorly understood. OBJECTIVES: This paper explores the food system determinants of food and nutrition security in urban informal settlements in LMICs with the goal of identifying effective approaches and entry points for policy and program. METHODS: Scoping review. Five databases were screened spanning the period 1995 to 2019. A total of 3748 records were assessed for inclusion based on title and abstract followed by 42 full text reviews. At least two reviewers assessed each record. Twenty-four final publications were included, coded, and synthesized. RESULTS: Factors influencing food security and nutrition in urban informal settlements can be organized into three interconnected levels. Macro-level factors include globalization, climate change, transnational food corporations, international treaties and regulations, global and national policies such as SDGs, insufficient social aid programs, and formalization or privatization. Meso-level factors include gender norms, inadequate infrastructure and services, insufficient transportation, informal food retailers, weak municipal policies, marketing strategies, and (lack of) employment. Micro-level factors comprise gender roles, cultural expectations, income, social networks, coping strategies, and food (in) security. CONCLUSIONS: Greater policy attention should focus at the meso-level, with priority investments in services and infrastructure within urban informal settlements. The role and engagement of the informal sector is an important consideration in improving the immediate food environment. Gender is also crucial. Women and girls have a central role in food provisioning but are more vulnerable to various forms of malnutrition. Future research should include context-specific studies in LMIC cities as well as promoting policy change using a participatory and gender transformative approach.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".