Socioeconomic Predictors of Health and Development in Middle Childhood: Variations by Socioeconomic Status Measure and Race
Bibliographic record
Abstract
BACKGROUND: Socioeconomically disadvantaged children have poorer physical and mental health and lower social and school/academic functioning compared to children with higher socioeconomic status (SES). These associations are not static but may vary by choice of SES indicator and child race/ethnicity. However, little is known about these associations in middle-childhood, a distinct and critical developmental period. We explore these associations in a small exploratory study designed to examine associations between SES and child developmental outcomes in middle childhood. MATERIALS AND METHODS: We recruited 60 families with a child between 8-12 years of age from the San Francisco Bay area September 2005-June 2006. The MacArthur Health and Behavior Questionnaire was used to assess health and adaptive functioning across four developmental domains: physical health, mental health, social functioning, and school/academic functioning. We examined a range of SES measures including continuous and categorical assessments of poverty, income, wealth, maternal and overall family educational attainment, subjective social status, and cumulative social risk. A series of multivariate ordinary least squares regressions was performed on the total sample and within race-specific groups. RESULTS: Although the long-recognized, graded relations among SES and outcomes were present, associations employing categorical representations of SES were far more pervasive; and stronger in magnitude. Wealth and highest degree earned in the family showed the strongest associations across virtually all health/functioning domains. Health and functioning was more strongly associated with educational attainment among Whites and financial resources among Blacks. Among Whites more wealth was associated with worse outcomes. CONCLUSIONS: Further research is needed to confirm the study findings. However, this study raises important questions about the measurement of SES for studying disparities in child health and developmental outcomes. This initial research suggests that improvements in health and functioning in middle childhood may require more significant status transitions; more targeted social interventions to address racial/ethnic disparities in child health and developmental outcomes; and a need to intervene on adversities facing affluent youth, a potentially hidden yet vulnerable group in middle childhood.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".