PROTOCOL: Impact of the food environment on diet‐related health outcomes in school‐age children and adolescents in low‐ and middle‐income countries: a systematic review
Bibliographic record
Abstract
Background The problem, condition or issueCurrent estimates indicate that some form of malnutrition (undernutrition, overweight or obesity, and poor dietary habits) affects one in three people worldwide (International Food Policy Research Institute, 2016), and the 2016 Global Burden of Disease study has placed poor dietary habits as one of the leading risk factors for mortality globally (Collaborators GBDRF, 2017).Over the past decade, there has been great momentum around addressing malnutrition in all its forms and commitment to actions that can accelerate progress to reduce its associated burden of morbidity and mortality.In 2012, the World Health Assembly (WHA) adopted the 2025 Global Targets for Maternal, Infant and Young Child Nutrition and in 2013, WHA adopted targets for non-communicable diseases (NCDs), including several nutrition-relevant targets (International Food Policy Research Institute, 2016).More recently, the United Nations elevated its efforts through a global declaration of 17 Sustainable Development Goals (SDGs), where at least 12 of the 17 goals feature indicators relevant to nutrition.In line with these targets, the decade of 2016-2025 has been declared the Decade of Action on Nutrition (International Food Policy Research Institute, 2016).To this end, prioritizing critical actions to address school-age children and adolescent nutrition, is necessary to achieve these milestones.At the forefront of malnutrition and poor dietary intake is the food system. The food systemAccording to the Food and Agriculture Organization (FAO) High Level Panel of Global Food and Nutrition Security, the food system is defined as 'a system that embraces all the elements (environment, people, inputs, processes, infrastructure, institutions, markets and trade) and activities that relate to the production, processing, distribution and marketing, preparation and consumption of food and the outputs of these activities, including socio-economic and environmental outcomes' (High Level Panel of Experts, 2017).Importantly, this group identified three food system typologies (i.e.traditional, mixed and modern), based on distinct inputs (natural resources, human capital, physical capital, agriculture and food technology), outputs (food purchasing patterns, diet, health and environmental sustainability) and processes (food production, supply chains and the food environment) Table 1.The transition from traditional to industrial food systems has been linked to urbanization, policy liberalization, agricultural productivity and income growth.In addition, the Global Nutrition Report, (International Food Policy Research Institute, 2015) defined two additional food system typologies (emerging and transitioning food systems), which are variations of the mixed food system, often observed in low-and middle-income countries (LMICs).Importantly, multiple types of food systems, and their associated food supply chains and food environments can co-exist within a single country simultaneously.Within traditional (or rural) food systems, there is a greater proportion of informal food markets (i.e.wet markets, mobile street vendors), compared to formal food outlets, as food is
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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 | 0.034 | 0.073 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.015 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.101 | 0.009 |
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".