Exploring socioeconomic status, lifestyle factors, and cardiometabolic disease outcomes in the United States: insights from a population-based cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Cardiometabolic diseases are a major global health concern. This study aims to identify areas for targeted interventions and investigate the impact of socioeconomic status and lifestyle as a potential mediator in the context of the US. METHODS: Our study analyzed data from the Health Information National Trends Survey 5, a nationwide survey by the National Cancer Institute. Using standardized scales and questions, we examined cardiometabolic disease outcomes, lifestyle factors, and socioeconomic status of non-institutionalized civilians aged 18 + in the US. We analyzed the data using structural equation modelling. RESULTS: Our findings show that socioeconomic status and lifestyle significantly predict cardiometabolic disease outcomes. However, our analysis did not support lifestyle as the primary mediating factor in the association between socioeconomic status and cardiometabolic diseases, suggesting that other factors may significantly influence this relationship. CONCLUSIONS: Cardiometabolic diseases require lifestyle and structural interventions addressing socioeconomic factors. Policymakers must consider multifaceted factors to prevent, detect, and manage these diseases effectively and equitably.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".