The influence of food environments on dietary behaviour and nutrition in Southeast Asia: A systematic scoping review
Bibliographic record
Abstract
Background: Food environments are crucial spaces within the food system for understanding and addressing many of the shared drivers of malnutrition. In recent years, food environment research has grown rapidly, however, definitions, measures, and methods remain highly inconsistent, leading to a body of literature that is notably heterogeneous and poorly understood, particularly within regions of the Asia-Pacific. Aim: This scoping review aims to synthesize the nature, extent, and range of published literature surrounding the role of the food environment on influencing dietary behaviour and nutrition in Southeast Asia. Methods: A systematic search of 5 databases was conducted following PRISMA guidelines for scoping reviews. Eligible studies included peer-reviewed research with adult participants living in Southeast Asia that examined the food environment as a determinant of dietary behaviour or nutrition. Results: A total of 45 articles were included. Overall, studies indicated that dietary behaviours in Southeast Asia were primarily driven by social, cultural, and economic factors rather than physical (e.g. geographical) features of food environments. Food price and affordability were most consistently identified as key barriers to achieving healthy diets. Conclusion: This work contributes to the establishment of more robust conceptualizations of food environments within diverse settings which may aid future policymakers and researchers identify and address the barriers or obstacles impacting nutrition and food security in their communities. Further research is needed to strengthen this knowledge, particularly research that explicitly explores the macro-level mechanisms and pathways that influence diet and nutrition outcomes.
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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 | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".