Diet and Food and Nutrition Insecurity and Cardiometabolic Disease
Bibliographic record
Abstract
Poor nutrition is the leading cause of poor health, health care spending, and lost productivity in the United States and globally, which acts through cardiometabolic diseases as precursors to cardiovascular disease, cancer, and other conditions. There is great interest in how the social determinants of health (the conditions in which people are born, live, work, develop, and age) impact cardiometabolic disease. Food insecurity is an example of a powerful social determinant of health that impacts health outcomes. Nutrition insecurity, a distinct but related concept to food insecurity, is a direct determinant of health. In this article, we provide an overview of how diet in early life relates to cardiometabolic disease and then continue to focus on the concepts of food insecurity and nutrition insecurity. In the discussions herein we make important distinctions between the concepts of food insecurity and nutrition insecurity and provide a review of their concepts, histories, measurement and assessment devices, trends and prevalence, and links to health and health disparities. The discussions here set the stage for future research and practice to directly address the negative consequences of food and nutrition insecurity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".