Characterizing adverse prenatal and postnatal experiences in children
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Prenatal and postnatal adversities, including prenatal alcohol exposure (PAE), prenatal exposure to other substances, toxic stress, lack of adequate resources, and postnatal abuse or neglect, often co-occur. These exposures can have cumulative effects, or interact with each other, leading to worse outcomes than single exposures. However, given their complexity and heterogeneity, exposures can be difficult to characterize. Clinical services and research often overlook additional exposures and attribute outcomes solely to one factor. METHODS: We propose a framework for characterizing adverse prenatal and postnatal exposures and apply it to a cohort of 77 children. Our approach considers type, timing, and frequency to quantify PAE, other prenatal substance exposure, prenatal toxic stress, postnatal threat (harm or threat of harm), and postnatal deprivation (failure to meet basic needs) using a 4-point Likert-type scale. Postnatal deprivation and harm were separated into early (<24 months of age) and late (≥24 months) time periods, giving seven exposure variables. Exposures were ascertained via health records, child welfare records, interviews with birth parents, caregivers, and/or close family/friends. RESULTS: Nearly all children had co-occurring prenatal exposures, and two-thirds had both prenatal and postnatal adversities. Children with high PAE were more likely to experience late postnatal adversities, and children with other prenatal substance exposure were more likely to have early postnatal deprivation. Postnatal adversities were more likely to co-occur. CONCLUSION: This framework provides a comprehensive picture of a child's adverse exposures, which can inform assessment and intervention approaches and policy and will be useful for future research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it