Before it's too late: Enhancing the early detection and prevention of long-term placement disruption
Bibliographic record
Abstract
In this paper, we examine some of the principal findings of a recent 3-year longitudinal study into foster care in South Australia and their implications for addressing the needs of children who experience high rates of placement disruption while in care. A critical finding of this study was that many of the most serious problems in foster care, such as repeated placement disruption, can be anticipated and predicted with considerable accuracy. Children who experience a disproportionately higher rate of placement disruption appear to be readily identifiable at intake. In addition, there appears to be an approximate threshold or point beyond which children subject to placement disruption begin to experience significant deterioration in their psychosocial functioning. This predictability of outcomes suggests the possibility of the early detection of children most at risk in foster care, and a means of identifying children failing to adapt to care. We believe that the extension of this form of analysis to other Australian states, for example, through the development of nationally agreed-upon definitions of ‘at risk’ and ‘harm due to disruption’ in foster care, may significantly enhance current attempts to evaluate and target treatment programs designed for children with challenging behaviours.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".