Evaluation of a Newborn Screen for Predicting Out-of-Home Placement
Bibliographic record
Abstract
A newborn screen designed to predict family risk was examined to: (a) determine whether all families with newborns were screened; (b) evaluate its predictive validity for identifying risk of out-of-home placement, as a proxy for maltreatment; (c) determine which items were most predictive of out-of-home placement. All infants born in Manitoba, Canada from 2000 to 2002 were followed until March 31, 2004 (N = 40,886) by linking four population-based data sets: (a) newborn screening data on biological, psychological, and social risks; (b) population registry data on demographics; (c) hospital discharge data on newborn birth records; (d) data on children entering out-of-home care. Of the study population, 18.4% were not screened and 3.0% were placed in out-of-home care at least once during the study period. Infants not screened were twice as likely to enter care compared to those screened (4.9% vs. 2.5%). Infants screening at risk were 15 times more likely to enter care than those screening "not at risk." Sensitivity and specificity of the screen were 77.6% and 83.3%, respectively. Screening efforts to identify vulnerable families missed a substantial portion of families needing support. The screening tool demonstrated moderate predictive validity for identifying children at risk of entering care in the first years of life.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".