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Record W3084152259 · doi:10.1186/s40795-020-00368-1

Social network characteristics are correlated with dietary patterns among middle aged and older South Asians living in the United States (U.S.)

2020· article· en· W3084152259 on OpenAlex
Sameera A. Talegawkar, Nicola Lancki, Yichen Jin, Juned Siddique, Meghana Gadgil, Alka M. Kanaya, John A. Schneider, Linda Van Horn, Lawrence de Koning, Namratha R. Kandula

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBMC Nutrition · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsCalgary Laboratory ServicesUniversity of Calgary
FundersNational Center for Research ResourcesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteUniversity of California, San FranciscoNational Institutes of Health
KeywordsSocial network (sociolinguistics)MedicineEnvironmental healthEthnic groupFood frequency questionnaireSocial network analysisDemographyGerontologySocial media

Abstract

fetched live from OpenAlex

Abstract Background Social and cultural norms, operating through social networks, may influence an individual’s dietary choices. We examined correlations between social network characteristics and dietary patterns among South Asians in the United States (U.S.) Methods Data from the Mediators of Atherosclerosis in South Asians Living in America (MASALA) Social Network study were analyzed among 756 participants (mean age = 59 y standard deviation [SD] = 9 y; 44% women). A culturally adapted, validated food frequency questionnaire was used for dietary assessment. A posteriori dietary patterns using principal component analysis were named 1) animal protein, 2) fried snacks, sweets and high-fat dairy, and 3) fruits, vegetables, nuts and legumes. Social network characteristics were assessed using a standard egocentric approach, where participants (egos) self-reported data on perceived dietary habits of their network members. Partial correlations between social network characteristics and egos’ dietary patterns were examined. Results The mean social network size of egos was 4.2 (SD = 1.1), with high proportion of network members being family (72%), South Asian ethnicity (89%), and half having daily contact. Animal protein pattern scores were negatively correlated with fruits and cooked vegetables consumption of network. Fried snacks, sweets and high-fat dairy pattern scores were positively correlated with sugar-sweetened beverages, South Asian sweets, fried/fast foods and ghee (clarified butter) consumption of network. Fruits, vegetables, nuts and legumes pattern scores were positively correlated with vegetables, fruits, and brown rice/quinoa consumption of network. Conclusions Network member characteristics and their perceived dietary behaviors were correlated with dietary patterns of egos. Dietary intervention studies among South Asians should consider social network characteristics as candidate components for dietary intervention.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.277
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it