Characterizing adverse prenatal and postnatal experiences in children
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".