Re-envisaging child protection contacts as an early prevention opportunity to support child development and well-being: an Australian data linkage study
Bibliographic record
Abstract
OBJECTIVES: To quantify developmental vulnerability at age 5 by child protection contacts before school in two Australian states. METHODS: All children with birth, child protection and/or 2009, 2012, 2015 and 2018 Australian Early Development Census (AEDC) data in New South Wales (NSW) and South Australia (SA) were grouped according to child protection contact before school: no contact, child protection reports, screened-in reports, investigations, substantiations and out-of-home care (OOHC). The outcome was developmental vulnerability on ≥1 AEDC domains or medically diagnosed conditions with support needs at school entry. RESULTS: 56 650 (14.2%) NSW children and 12 617 (15.6%) SA children had ≥1 child protection contact before school. Developmental vulnerability on ≥1 domains or medically diagnosed conditions was lowest in the no child protection group (NSW, 21-22%; SA, 24-25%), with progressively higher risk in the child protection report (NSW, 35%; SA, 41-46%) through to the OOHC (NSW, 50-54%; SA, 59-66%) groups in all AEDC years. Developmental risk was higher among children aged <2 years at first contact and those with more reports. Children with only one child protection report before school had approximately 65% higher developmental risk than the no child protection group in both states. CONCLUSIONS: A single child protection report before school was an early indicator of higher developmental risk at age 5, with higher developmental risks among children with earlier, more serious and frequent child protection contacts. Beyond child safety screening, child protection reports represent an opportunity to mobilise early health and social support for children with developmental support needs.
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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 | 0.041 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".