165: Profiles of Environmental Risk Impact Child Cognition Via Maternal Sensitivity and Human Capital Investments
Bibliographic record
Abstract
There is evidence to suggest that socioeconomic status impacts neurocognitive functioning via family investment in child human capital. However, the role of parenting remains surprisingly equivocal, largely due to discrepancies in operationalization and measurement. Moreover, many studies examining the social determinants of cognitive development rely on simple metrics of socioeconomic status or risk. The current study a) identifies multiple “family risk profiles” across several domains of adversity and b) examines the mediating role of human capital investments and sensitive maternal parenting behavior (coded from videotaped observations). Family profiles of socioeconomic and contextual risk at two months of age were identified using participants from the Kids, Families and Places Study prospective birth-cohort. Subsequently, the impact of these profiles on child cognitive development via maternal sensitivity and human capital (material) investments was evaluated. Parents were contacted through a universal health screen seven days following childbirth (n=501) and followed until children were >4.5 years of age. Latent Class Analysis revealed that families fell into one of four profiles: 1) multilevel risk, 12.0% of sample, 2) maternal abuse history, 15.6%, 3) low-SES and immigrant status, 27.7% or 4) low-risk, 44.7%. Children in the multilevel risk and low-SES immigrant profiles had the poorest outcomes at 4.5 years. Between 35% and 56% of the effect of risk profile on cognitive outcomes was mediated by investments in child development, while 21% to 44% was mediated by maternal sensitivity, both measured at 18 months of age. Findings highlight the importance of material and interpersonal pathways through which socioeconomic and environmental disadvantage impact cognitive development. It will be argued that maternal sensitivity can be conceptualized as an alternative human capital investment, that is, a lived experience that promotes healthy development and successful adaptation across the life course.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".