A systematic review of food deserts, 1966-2007.
Bibliographic record
Abstract
INTRODUCTION: "Food deserts," areas characterized by poor access to healthy and affordable food, may contribute to social and spatial disparities in diet and diet-related health outcomes. However, the extent to which food deserts exist is debated. We review the evidence for the existence of food deserts in socioeconomically disadvantaged areas. METHODS: We conducted a systematic review of primary, quantitative, observational studies, published in English or French, that used geographic or market-basket approaches in high-income countries. The literature search included electronic and hand searches and peer-reviewed and grey literature from 1966 through 2007. We also contacted key researchers to identify other studies. We analyzed the findings and quality of the studies qualitatively. RESULTS: Forty-nine studies in 5 countries met inclusion criteria; the amount and consistency of the evidence varied by country. These studies were a mix of geographic and market-basket approaches, but the methodologic quality of studies and completeness of reported findings were mixed. We found clear evidence for disparities in food access in the United States by income and race. Findings from other high-income countries were sparse and equivocal. CONCLUSION: This review suggests that food deserts exist in the United States, where area-level deprivation compounds individual disadvantage. Evidence for the existence of food deserts in other high-income nations is weak.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.015 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".