What does international evidence tell us about the health of people with childhood social care (CSC) experiences?
Bibliographic record
Abstract
Abstract International research into the health of people with childhood social care (CSC) experiences has found evidence of worse mental health and emotional-behavioural wellbeing, higher rates of substance use, neurodevelopmental conditions, and avoidable mortality. Lower vaccination rates and worse dental health among those in care has also been reported in few countries. However, research results have been more mixed with regard to some physical health conditions, with higher prevalence of asthma and respiratory ill health among foster children reported in the US but no obvious differences found in the UK. Are there countries where these inequalities are less pronounced, and can we draw such conclusions based on available evidence? When making international comparisons or aiming to give policy advice, we also need to consider the quality of our evidence. Has this been based on small sample sizes or without comparison to other children and adjusting for relevant (socioeconomic) confounders? Most of our current knowledge is also cross-sectional and we do not know if some health conditions precede entry to care and may even be risk-factors for entering social care. Recent longitudinal and cross-sectoral data linkage programmes in many nations (Australia, UK, Canada) have a potential to change this and provide a foundation for evidence-based recommendations for policy and practice.
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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 | 0.040 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".