Identifying Opportunities for Strategic Policy Design to Address the Double Burden of Malnutrition through Healthier Retail Food: Protocol for South East Asia Obesogenic Food Environment (SEAOFE) Study
Bibliographic record
Abstract
Effective policies that address both the supply and demand dimensions of access to affordable, healthy foods are required for tackling malnutrition in South East Asia. This paper presents the Protocol for the South East Asia Obesogenic Food Environment (SEAOFE) study, which is designed to analyze the retail food environment, consumers' and retailers' perspectives regarding the retail food environment, and existing policies influencing food retail in four countries in South East Asia in order to develop evidence-informed policy recommendations. This study was designed as a mixed-methods sequential explanatory approach. The country sites are Malaysia, Indonesia, the Philippines, and Thailand. The proposed study consists of four phases. Phase One describes the characteristics of the current retail food environment using literature and data review. Phase Two interprets consumer experience in the retail food environment in selected urban poor communities using a consumer-intercept survey. This phase also assesses the retail food environment by adapting an in-store audit tool previously validated in higher-income countries. Phase Three identifies factors influencing food retailer decisions, perceptions, and attitudes toward food retail policies using semi-structured interviews with selected retailers. Phase Four recommends changes in the retail food environment using policy analysis and semi-structured interviews with key stakeholders. For the analysis of the quantitative data, descriptive statistics and multiple regression will be used, and thematic analysis will be used to process the qualitative data. This study will engage stakeholders throughout the research process to ensure that the design and methods used are sensitive to the local context.
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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 | 0.124 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.040 | 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".