Adverse Childhood Experiences in Infancy and Toddlerhood Predict Obesity and Health Outcomes in Middle Childhood
Bibliographic record
Abstract
BACKGROUND: The Adverse Childhood Experiences (ACEs) study articulated the negative effects of childhood trauma on adult weight and health. The purpose of the current study is to examine the associations between ACEs in infancy and toddlerhood and obesity and related health indicators in middle childhood. METHODS: We used data collected from a sample of low-income families enrolled in the national evaluation of Early Head Start (EHS). Data come from 1335 demographically diverse families collected at or near children's ages 1, 2, 3, and 11. An EHS-ACE index was created based on interview and observation items from data collected at ages 1, 2, and 3, which were averaged to represent exposure across infancy and toddlerhood. At age 11, children's height and weight were measured and parents were asked about their child's health. RESULTS: Children were exposed at rates of 30%, 28%, 15%, and 8% to one, two, three, and four or more EHS-ACEs, respectively. Logistic regressions revealed significant associations between EHS-ACEs in infancy/toddlerhood and obesity, respiratory problems, taking regular nonattention-related prescriptions, and the parent's global rating of children's health at age 11. Across all outcomes examined, children with four or more ACEs had the poorest health. Compared with children with no ACE exposure, the odds of each of the examined health outcomes were over twice as high for children who experienced four or more ACEs. CONCLUSIONS: Findings highlight that ACEs experienced very early in development are associated with children whose health is at risk later in 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 | 0.001 | 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) | 0.001 | 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".