Preventive Health Care Among Children of Women With Schizophrenia
Bibliographic record
Abstract
To compare well-baby visit and vaccination schedule adherence up to age 24 months in children of mothers with versus without schizophrenia. type B (DTaP-IPV-Hib), and measles, mumps, rubella (MMR). Cox proportional hazard regression models were adjusted for each of maternal sociodemographics, maternal health, and child health characteristics in blocks and all together in a fully adjusted model. About 50.3% of children with maternal schizophrenia had an enhanced 18-month well-baby visit versus 58.6% of those without, corresponding to 29.0 versus 33.9 visits/100 person-years (PY), a hazard ratio (HR) of 0.82 (95% CI, 0.76-0.89). The association was dampened after adjustment for maternal sociodemographics, maternal health, and child health factors in blocks and overall, with a fully adjusted HR of 0.91 (95% CI, 0.84-0.98). Full vaccine schedule adherence occurred in 40.0% of children with maternal schizophrenia versus 46.0% of those without (22.6 vs 25.9/100 PY), yielding a HR of 0.86 (95% CI, 0.78-0.94). The association was dampened when adjusted for maternal sociodemographics and child health characteristics and became nonsignificant when adjusted for maternal health characteristics. The fully adjusted HR was 0.95 (95% CI, 0.87-1.04). Increased efforts to ensure that children with maternal schizophrenia receive key early preventive health care services are warranted.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".