Socioeconomic status and executive function in early childhood: Exploring proximal mechanisms
Bibliographic record
Abstract
Although there is substantial evidence that socioeconomic status (SES) predicts children's executive function (EF), the mechanisms underlying this association are poorly understood. This study tested the utility of two theories proposed to link SES to children's EF: the family stress model and the family investment model. Data came from the Midwestern Infant Development Study (N = 151). To measure SES, parental education and income were assessed during pregnancy, and income was also assessed when children were 6 and 36 months old. Children's EF, operationalized as working memory/inhibitory control (WMIC) and self-control, was assessed at 36 months of age, along with potential mediators including maternal psychological distress, harsh parenting, and cognitive stimulation. Using structural equation modeling, we tested simultaneous pathways from SES to EF: (a) via maternal psychological distress to harsh parenting (family stress model) and (b) via cognitive stimulation (family investment model). Of the SES measures, lower education predicted poorer WMIC directly and indirectly via greater maternal psychological distress. Lower education also predicted poorer self-control via greater maternal psychological distress. This effect was partially suppressed by an indirect path from lower education to better self-control via greater psychological distress and increased harsh parenting. Cognitive stimulation did not act as a mediator. Income was not directly or indirectly associated with EF. These findings provide partial support for the family stress model and suggest that family functioning is an important proximal mechanism for children's EF development. This study also highlights the importance of considering SES as a multidimensional construct.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".