Social Network Characteristics Are Correlated with Dietary Patterns Among Middle Aged and Older South Asians (SA) Living in the United States (US) (P04-124-19)
Bibliographic record
Abstract
Social and cultural norms, operating through social networks, may influence an individual’s dietary choices. We examined the correlations among social network characteristics and dietary patterns among SA in the US. Data from the Mediators of Atherosclerosis in South Asians Living in America 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. Principal component analysis yielded, three dietary patterns named: “Animal protein”, “Fried snacks, sweets and high-fat dairy”, and “Fruits, vegetables, nuts and legumes”, based on contribution of food groups. 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. The mean social network size of egos was 4.2 (SD = 1.1), with high proportion of network members being family (72%), SA ethnicity (89%), and half having daily contact. Higher scores for the “Animal protein” pattern among egos, were negatively correlated with daily fruits and cooked vegetables consumption of their network. This pattern was also positively correlated with the proportion of network who weekly consumed non-South Asian foods, diet drinks, non-vegetarian foods, processed meat and fried foods, and dined out. Scores for the “Fried snacks, sweets and high-fat dairy” pattern were positively correlated with proportion of network who weekly consumed sugar-sweetened beverages, South Asian sweets, fried foods, fast foods and ghee (clarified butter). Higher scores for the “Fruits, vegetables, nuts and legumes” pattern were positively associated with proportion of network who daily consumed both raw and cooked vegetables and fruits, and brown rice/quinoa weekly. Network member characteristics and perceived dietary behaviors of network members were correlated with dietary patterns of SA in the US. Dietary intervention studies among SA should consider social network characteristics as candidate components for dietary intervention. National Institutes of Health.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".