Health of Newborns and Infants Born to Women With Disabilities: A Meta-analysis
Bibliographic record
Abstract
CONTEXT: Women with disabilities are at elevated risk for pregnancy, delivery, and postpartum complications. However, there has not been a synthesis of literature on the neonatal and infant health outcomes of their offspring. OBJECTIVE: We examined the association between maternal disability and risk for adverse neonatal and infant health outcomes. DATA SOURCES: Cumulative Index to Nursing and Allied Health Literature, Embase, Medline, and PsycINFO were searched from database inception to January 2020. STUDY SELECTION: Studies were included if they reported original data on the association between maternal physical, sensory, or intellectual and/or developmental disabilities and neonatal or infant health outcomes; had a referent group of women with no disabilities; were peer-reviewed journal articles or theses; and were written in English. DATA EXTRACTION: We used standardized instruments to extract data and assess study quality. DerSimonian and Laird random effects models were used for pooled analyses. RESULTS: Thirty-one studies, representing 20 distinct cohorts, met our inclusion criteria. Meta-analyses revealed that newborns of women with physical, sensory, and intellectual and/or developmental disabilities were at elevated risk for low birth weight and preterm birth, with smaller numbers of studies revealing elevated risk for other adverse neonatal and infant outcomes. LIMITATIONS: = 17), with lack of control for confounding a common limitation. CONCLUSIONS: In future work, researchers should explore the roles of tailored preconception and perinatal care, along with family-centered pediatric care particularly in the newborn period, in mitigating adverse outcomes among offspring of women with disabilities.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.057 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".