Supplemental Material for Choosing Immediate Over Delayed Gratification Correlates With Better School-Related Outcomes in a Sample of Children of Color From Low-Income Families

The purpose of this article is to demonstrate how equity audits can be used as a tool to expose disparate achievement in schools that, on the surface and to the public, appear quite similar. To that end, the researcher probed beyond surface-level performance composite scores into deeper, more hidden data associated with state-recognized "Honor Schools of Excellence." How is "excellence" defined and operationalized in these schools? Are these schools "excellent" for all students? Can a school really be classified by the state as "excellent" and yet still have significant "gaps" and disparities? If so, is the state's formula used to identify exemplary schools too simple, dogmatic, and institutionally flawed? Through the use of equity audits, quantitative data was collected to scan for systemic patterns of equity and inequity across multiple domains of student learning and activities within 24 elementary schools. The intent was to document and distinguish between schools that are promoting and supporting both academic excellence (small gap schools; SGS) and systemic equity and schools that are not (large gap schools; LGS). Results reveal that although demographic, teacher quality, and programmatic audits all indicated a fair amount of equity between SGS and LGS, the achievement audit between both types of schools indicated great disparities. By controlling for or eliminating some of the external variables and internal factors often cited for the achievement gaps between white middle-class children and children of color or children from low-income families, the findings from this study raise more questions than answers. Results do indicate that equity audits are a practical, easy-to-apply tool that educators can use to identify inequalities objectively.

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Medicaid, Low-Income Families, and Reimbursement

Pediatric News ◽

10.1016/s0031-398x(06)71583-1 ◽

2006 ◽

Vol 40 (12) ◽

pp. 68

Author(s):

BRYAN FINE

Keyword(s):

Low Income ◽

Low Income Families

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Demonstrating Nutrient Cost Gradients: A Brooklyn Case Study

International Journal for Vitamin and Nutrition Research ◽

10.1024/0300-9831/a000210 ◽

2014 ◽

Vol 84 (5-6) ◽

pp. 244-251 ◽

Cited By ~ 2

Author(s):

Robert J. Karp ◽

Gary Wong ◽

Marguerite Orsi

Keyword(s):

Low Income ◽

Energy Content ◽

Food Selection ◽

The United States ◽

United States Department ◽

Micronutrient Deficiencies ◽

Caloric Density ◽

Low Income Families ◽

The Cost ◽

The Impact

Abstract. Introduction: Foods dense in micronutrients are generally more expensive than those with higher energy content. These cost-differentials may put low-income families at risk of diminished micronutrient intake. Objectives: We sought to determine differences in the cost for iron, folate, and choline in foods available for purchase in a low-income community when assessed for energy content and serving size. Methods: Sixty-nine foods listed in the menu plans provided by the United States Department of Agriculture (USDA) for low-income families were considered, in 10 domains. The cost and micronutrient content for-energy and per-serving of these foods were determined for the three micronutrients. Exact Kruskal-Wallis tests were used for comparisons of energy costs; Spearman rho tests for comparisons of micronutrient content. Ninety families were interviewed in a pediatric clinic to assess the impact of food cost on food selection. Results: Significant differences between domains were shown for energy density with both cost-for-energy (p < 0.001) and cost-per-serving (p < 0.05) comparisons. All three micronutrient contents were significantly correlated with cost-for-energy (p < 0.01). Both iron and choline contents were significantly correlated with cost-per-serving (p < 0.05). Of the 90 families, 38 (42 %) worried about food costs; 40 (44 %) had chosen foods of high caloric density in response to that fear, and 29 of 40 families experiencing both worry and making such food selection. Conclusion: Adjustments to USDA meal plans using cost-for-energy analysis showed differentials for both energy and micronutrients. These differentials were reduced using cost-per-serving analysis, but were not eliminated. A substantial proportion of low-income families are vulnerable to micronutrient deficiencies.

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