An exploratory study of predictors of cognition in two low-income samples of infants across the first year of life
Bibliographic record
Abstract
OBJECTIVE: In this exploratory longitudinal study we assessed cognitive development in a community sample of infants born into predominantly low-income families from two different urban sites, to identify family and community factors that may associate with outcomes by 1 year of age. METHOD: Infant-mother dyads (n = 109) were recruited in Boston and Los Angeles community pediatric practices. Infant cognition was measured using the Mullen Scales of Early Learning when the infant was aged 2, 6, 9, and 12 months. Longitudinal linear mixed effects modeling and linear regression models explored potential predictors of cognitive outcomes. RESULTS: Cognitive scores were lower than the reference population mean at both 6 and 12 months. There were site differences in demographics and cognitive performance. Maternal education predicted expressive language in Boston, and speaking Spanish and lower rates of community poverty were associated with greater increases in overall cognition in Los Angeles. CONCLUSION: This exploratory study identified a number of drivers of child development that are both shared across cohorts and unique to specific community samples. Factors influencing heterogeneity within and across populations both may be important contributors to prevention and intervention in supporting healthy development among children.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".