Social and Behavioral Sciences Commons^™

Competing Infant Feeding Information In Mothers’ Networks: Advice That Supports V. Undermines Clinical Recommendations, Sato Ashida, Freda B. Lynn, Natalie A. Williams

Department of Child, Youth, and Family Studies: Faculty Publications

Objective: To identify the social contextual factors, specifically the presence of information that supports v. undermines clinical recommendations, associated with infant feeding behaviours among mothers in low-income areas.

Design: Cross-sectional survey evaluating social support networks and social relationships involved in providing care to the infant along with feeding beliefs and practices.

Setting: Out-patient paediatric and government-funded (Women, Infants, and Children) clinics in an urban, low-income area of the south-eastern USA.

Subjects: Eighty-one low-income mothers of infants between 0 and 12 months old.

Results: Most mothers reported receiving both supportive and undermining advice. The presence of …

Competing Infant Feeding Information In Mothers’ Networks: Advice That Supports V. Undermines Clinical Recommendations, Sato Ashida, Freda B. Lynn, Natalie A. Williams, Ellen J. Schafer

Department of Child, Youth, and Family Studies: Faculty Publications

Objective: To identify the social contextual factors, specifically the presence of information that supports v. undermines clinical recommendations, associated with infant feeding behaviours among mothers in low-income areas.

Design: Cross-sectional survey evaluating social support networks and social relationships involved in providing care to the infant along with feeding beliefs and practices.

Setting: Out-patient paediatric and government-funded (Women, Infants, and Children) clinics in an urban, low-income area of the south-eastern USA.

Subjects: Eighty-one low-income mothers of infants between 0 and 12 months old.

Results: Most mothers reported receiving both supportive and undermining advice. The presence …

Global Network Inference From Ego Network Samples: Testing A Simulation Approach, Jeffrey A. Smith

Department of Sociology: Faculty Publications

Network sampling poses a radical idea: that it is possible to measure global network structure without the full population coverage assumed in most network studies. Network sampling is only useful, however, if a researcher can produce accurate global network estimates. This article explores the practicality of making network inference, focusing on the approach introduced in Smith (2012). The method uses sampled ego network data and simulation techniques to make inference about the global features of the true, unknown network. The validity check here includes more difficult scenarios than previous tests, including those that go beyond the initial scope conditions of …