Socioeconomic Disadvantage in Infancy and Academic and Self-Regulation Outcomes
Bibliographic record
Abstract
OBJECTIVES: A comprehensive understanding of how timing of exposure to disadvantage affects long-term developmental risk is needed for greater precision in child health policy. We investigated whether socioeconomic disadvantage in infancy (age 0-1 years) directly affects academic and self-regulation problems in late childhood (age 10-12 years), independent of disadvantage at school entry (age 4-6 years). METHODS: = 5107). Generalized linear models were used to estimate the crude and adjusted effects. Marginal structural models were used to estimate the controlled direct effect of socioeconomic disadvantage in infancy on academic and self-regulation outcomes in late childhood, independent of disadvantage at school entry. RESULTS: In both cohorts, socioeconomic disadvantage in infancy and at school entry was associated with poorer academic and self-regulation outcomes. Socioeconomic disadvantage in infancy had a direct effect on academic outcomes not mediated by disadvantage at school entry (ATP: risk ratio [RR] = 1.42; 95% confidence interval [CI]: 1.09-1.86; LSAC: RR = 1.87; 95% CI: 1.52-2.31). Little evidence was found for a direct effect of disadvantage in infancy on self-regulation (ATP: RR = 1.22; 95% CI: 0.89-1.65; LSAC: RR = 1.19; 95% CI: 0.95-1.49). CONCLUSIONS: Socioeconomic disadvantage in infancy had a direct effect on academic but not self-regulation outcomes in late childhood. More precise public policy responses are needed that consider both the timing of children's exposure to disadvantage and the specific developmental domain impacted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".