Early life child protection contacts and developmental risk at age five: a whole-of-population cohort study of 479,413 children in two Australian states.
Bibliographic record
Abstract
IntroductionChildren who experience maltreatment have worse health and development outcomes than other children. Early prevention relies on opportunities to respond to system contacts that reliably indicate the population burden of children’s future developmental risk. Objectives and ApproachWe quantified the population burden of developmental vulnerability at age five by the type, timing, and frequency of child protection contacts before school, among children in two Australian states. We used linked whole-population births, child protection and Australian Early Development Census (AEDC) data (2009-2018 cycles) in New South Wales (NSW) and South Australia (SA). Results56,650/398,702 (14%) NSW and 12,617/80,731 (16%) SA children had ≥1 child protection contacts before school. The risk of developmental vulnerability on ≥1 domains was lowest in the no child protection group (NSW, 17-18%; SA, 19%), with higher risks in the child protection report (NSW, 28-29%; SA, 32-35%) through to the OOHC (NSW, 35-38%; SA, 39-50%) groups, with a similar pattern for the risk of medically diagnosed conditions. Children with only one child protection report before school had a higher developmental risk than the no child protection group (NSW, 34% versus 21%; SA, 42% versus 24%). Conclusions/ImplicationsEven a single child protection report in the first 2000 days of children’s lives was a robust indicator of developmental risk at age five, with higher developmental risks among children with more serious child protection contacts before school. Child protection reports represent an under-utilised asset to inform early universal and targeted support from health, human and early education services.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".