Environmental approaches to promote healthy eating: Is ensuring affordability and availability enough?
Bibliographic record
Abstract
Pablo Monsivais and colleagues reflect on the evidence for interventions to improve access to healthy food and discuss considerations for evidence generation Improving diet is a key goal of public health, as a substantial fraction of global morbidity and mortality is attributable to dietary imbalances.1 These imbalances include insufficient consumption of vegetables, fruits, and whole grains, and excessive intake of refined carbohydrates and meat. Moreover, inequities in health are driven in part by inequities in diet, and tackling them is a key dimension to improving diet and health at the population level. The past 20 years have seen increasing concern over structural factors that promote unhealthy dietary patterns and undermine the adoption of healthy eating. This trend has paralleled a growing understanding of the multifactorial “causes of the causes” of the modern pandemics of obesity and non-communicable disease,2 and interest in the physical, economic, and social environments that cue and shape behavioural risk factors.34 For food selection and diet specifically, there is recognition of the importance of affordability and availability, two dimensions of a wider conceptualisation of food access (box 1).5 The general consideration of “access to healthy food” is now a central pillar of policy, systems, and environments (PSE) interventions7 as well as so called “whole systems” approaches8 to improve nutrition and reduce obesity and chronic disease. As policy makers and communities act to forge more healthful, sustainable, and equitable food systems and environments, researchers recognise the uneven evidence base and debate the importance of economic and geographical factors as population level determinants of diet and health. Box 1 ### Access, affordability, and availabilityRETURN TO TEXT
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.014 |
| 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".