What Do Trajectories of Childhood Socioeconomic Status Tell Us About Markers of Cardiovascular Health in Adolescence?
Bibliographic record
Abstract
OBJECTIVE: The current study examined trajectories of socioeconomic status (SES) throughout childhood and their relationship to markers of cardiovascular health in adolescence. The goal was to determine whether early-life SES, current SES, cumulative SES, and/or social mobility best explained the relationship between SES experiences across an adolescent's life span and current blood pressure (BP), heart rate (HR), and body mass index (BMI). DESIGN: One hundred two adolescents completed cardiovascular health assessments including systolic blood pressure, diastolic blood pressure, HR, and BMI. Parents reported on family SES, indicating the number of bedrooms in the family home for each year of the child's life. RESULTS: Using Jones, Nagin, and Roeder's semiparametric group-based method, four distinct trajectories of childhood SES were identified. Trajectory groups were differentially related to adolescents' systolic blood pressure and diastolic blood pressure. A trajectory showing low early-life SES that increased through childhood was associated with the highest BP in adolescence. Partial correlation analyses specifically examining the various life-course scenarios similarly indicated that early-life SES was the strongest predictor of adolescents' BP. Trajectories of childhood SES were unrelated to HR and BMI. CONCLUSIONS: Of the life-course models that we tested, an early-life SES model best explained adolescents' current BP. These findings point toward early-life developmental processes as potential candidates for explaining the relationship between SES and risk factors related to cardiovascular disease. They suggest that interventions designed to reduce SES health disparities should take place early in a child's life.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".