Understanding Foster Placement Instability for Looked After Children: A Systematic Review and Narrative Synthesis of Quantitative and Qualitative Evidence
Bibliographic record
Abstract
Ensuring the stability of foster placements for looked after children is a priority for social services. Many previous studies have highlighted the negative psychological, social and academic consequences of placement breakdown for foster children, but less is known about how services can effectively promote placement stability. A systematic review and narrative synthesis of research examining correlates of placement moves and breakdown were undertaken in order to inform practice in this area. Qualitative studies were included alongside quantitative research, providing additional insights into the processes that facilitate and impede placement stability. Correlates of increased placement instability with the strongest evidence included older age of children, externalising behaviour, longer total time in care, residential care as first placement setting, separation from siblings, foster-care versus kinship care and experience of multiple social workers. Key protective factors included placements with siblings, placements with older foster-carers, more experienced foster-carers with strong parenting skills, and placements where foster-carers provide opportunities for children to develop intellectually. Following from these findings, a conceptual framework is proposed that distinguishes vulnerability and protective factors as well as background and immediate factors. Implications for front line social work practice, including the development of manualised tools, are discussed.
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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 | 0.021 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".