Behavioral Risk Assessment From Newborn to Preschool: The Value of Older Siblings
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to examine the plausibility of a risk prediction tool in infancy for school-entry emotional and behavioral problems. Familial aggregation has been operationalized previously as maternal psychopathology. The hypothesis was tested that older sibling (OS) psychopathology, as an indicator of familial aggregation, would enable a fair level of risk prediction compared with previous research, when combined with traditional risk factors. METHODS: = 2.00 months), all of whom had OSs. Infants were followed up 4.5 years later when both parents provided ratings of emotional and behavioral problems. Multiple regression and receiver operating characteristic curve analyses were conducted for emotional, conduct, and attention problems separately. RESULTS: The emotional and behavioral problems of OSs at infancy were the strongest predictors of the same problems in target children 4.5 years later. Other risk factors, including maternal depression and socioeconomic status provided extra, but weak, significant prediction. The area under the receiver operating characteristic curve for emotional and conduct problems yielded a fair prediction. CONCLUSIONS: This study is the first to offer a fair degree of prediction from risk factors at birth to school-entry emotional and behavioral problems. This degree of prediction was achieved with the inclusion of the emotional and behavioral problems of OSs (thus limiting generalizability to children with OSs). The inclusion of OS psychopathology raises risk prediction to a fair level.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".