Structural measures of personal networks predict migrants' cultural backgrounds: an explanation from Grid/Group theory.

Culture and social structure are not separated analytical domains but intertwined phenomena observable in personal networks. Drawing on a personal networks dataset of migrants in the United States and Spain, we show that the country of origin, a proxy for diverse languages and cultural institutions, and religion may be predicted by specific combinations of personal network structural measures (closeness, clustering, betweenness, average degree, etc). We obtain similar results applying three different methods (a multinomial logistic regression, a Random Forest algorithm, and an artificial neural network). This finding is explained within the framework of the Grid/Group theory that has long posed the interdependence of social structural and cultural features of human groups.

Open-ended interview questions and saturation.

Sample size determination for open-ended questions or qualitative interviews relies primarily on custom and finding the point where little new information is obtained (thematic saturation). Here, we propose and test a refined definition of saturation as obtaining the most salient items in a set of qualitative interviews (where items can be material things or concepts, depending on the topic of study) rather than attempting to obtain all the items. Salient items have higher prevalence and are more culturally important. To do this, we explore saturation, salience, sample size, and domain size in 28 sets of interviews in which respondents were asked to list all the things they could think of in one of 18 topical domains. The domains-like kinds of fruits (highly bounded) and things that mothers do (unbounded)-varied greatly in size. The datasets comprise 20-99 interviews each (1,147 total interviews). When saturation was defined as the point where less than one new item per person would be expected, the median sample size for reaching saturation was 75 (range = 15-194). Thematic saturation was, as expected, related to domain size. It was also related to the amount of information contributed by each respondent but, unexpectedly, was reached more quickly when respondents contributed less information. In contrast, a greater amount of information per person increased the retrieval of salient items. Even small samples (n = 10) produced 95% of the most salient ideas with exhaustive listing, but only 53% of those items were captured with limited responses per person (three). For most domains, item salience appeared to be a more useful concept for thinking about sample size adequacy than finding the point of thematic saturation. Thus, we advance the concept of saturation in salience and emphasize probing to increase the amount of information collected per respondent to increase sample efficiency.

The painful tweet: text, sentiment, and community structure analyses of tweets pertaining to pain.

BACKGROUND: Despite the widespread popularity of social media, little is known about the extent or context of pain-related posts by users of those media. OBJECTIVE: The aim was to examine the type, context, and dissemination of pain-related tweets. METHODS: We used content analysis of pain-related tweets from 50 cities to unobtrusively explore the meanings and patterns of communications about pain. Content was examined by location and time of day, as well as within the context of online social networks. RESULTS: The most common terms published in conjunction with the term "pain" included feel (n=1504), don't (n=702), and love (n=649). The proportion of tweets with positive sentiment ranged from 13% in Manila to 56% in Los Angeles, CA, with a median of 29% across cities. Temporally, the proportion of tweets with positive sentiment ranged from 24% at 1600 to 38% at 2400, with a median of 32%. The Twitter-based social networks pertaining to pain exhibited greater sparsity and lower connectedness than did those social networks pertaining to common terms such as apple, Manchester United, and Obama. The number of word clusters in proportion to node count was greater for emotion terms such as tired (0.45), happy (0.43), and sad (0.4) when compared with objective terms such as apple (0.26), Manchester United (0.14), and Obama (0.25). CONCLUSIONS: Taken together, our results suggest that pain-related tweets carry special characteristics reflecting unique content and their communication among tweeters. Further work will explore how geopolitical events and seasonal changes affect tweeters' perceptions of pain and how such perceptions may affect therapies for pain.

The science in social science.

A recent poll showed that most people think of science as technology and engineering--life-saving drugs, computers, space exploration, and so on. This was, in fact, the promise of the founders of modern science in the 17th century. It is less commonly understood that social and behavioral sciences have also produced technologies and engineering that dominate our everyday lives. These include polling, marketing, management, insurance, and public health programs.

Women's social networks and birth attendant decisions: application of the network-episode model.

This paper examines the association of women's social networks with the use of skilled birth attendants in uncomplicated pregnancy and childbirth in Matlab, Bangladesh. The network-episode model was applied to determine if network structure variables (density/kinship homogeneity/strength of ties) together with network content (endorsement for or against a particular type of birth attendant) explain the type of birth attendant used by women above and beyond the variance explained by women's individual attributes. Data were collected by interviewing a representative sample of 246 women, 18-45 years of age, using survey and social network methods between October and December 2008. Logistic regression models were used to examine the associations. Results suggest that the structural properties of networks did not add to explanatory value but instead network content or the perceived advice of network members add significantly to the explanation of variation in service use. Testing aggregate network variables at the individual level extends the ability of the individual profile matrix to explain outcomes. Community health education and mobilization interventions attempting to increase demand for skilled attendants need to reflect the centrality of kinship networks to women in Bangladesh and the likelihood of women to heed the advice of their network of advisors with regard to place of birth.

Counting hard-to-count populations: the network scale-up method for public health.

Estimating sizes of hidden or hard-to-reach populations is an important problem in public health. For example, estimates of the sizes of populations at highest risk for HIV and AIDS are needed for designing, evaluating and allocating funding for treatment and prevention programmes. A promising approach to size estimation, relatively new to public health, is the network scale-up method (NSUM), involving two steps: estimating the personal network size of the members of a random sample of a total population and, with this information, estimating the number of members of a hidden subpopulation of the total population. We describe the method, including two approaches to estimating personal network sizes (summation and known population). We discuss the strengths and weaknesses of each approach and provide examples of international applications of the NSUM in public health. We conclude with recommendations for future research and evaluation.

Skin color, social classification, and blood pressure in southeastern Puerto Rico.

OBJECTIVES: We tested competing hypotheses for the skin color-blood pressure relationship by analyzing the association between blood pressure and 2 skin color variables: skin pigmentation and social classification. METHODS: We measured skin pigmentation by reflectance spectrophotometry and social classification by linking respondents to ethnographic data on the cultural model of "color" in southeastern Puerto Rico. We used multiple regression analysis to test the associations between these variables and blood pressure in a community-based sample of Puerto Rican adults aged 25-55 years (n=100). Regression models included age, gender, body mass index (BMI), self-reported use of antihypertensive medication, and socioeconomic status (SES). RESULTS: Social classification, but not skin pigmentation, is associated with systolic and diastolic blood pressure through a statistical interaction with SES, independent of age, gender, BMI, self-reported use of antihypertensive medication, and skin reflectance. CONCLUSION: Our findings suggest that sociocultural processes mediate the relationship between skin color and blood pressure. They also help to clarify the meaning and measurement of skin color and "race" as social variables in health research.