197 Linkage between national social care and health data for unaccompanied asylum-seeking children in England, 2005-2021
Bibliographic record
Abstract
Abstract OP 31: Refugees and Asylum Seekers 6, B207 (FCSH), September 5, 2025, 09:00 - 10:00 Aims Unaccompanied Asylum-Seeking Children (UASCs) constitute one of the most vulnerable populations in the UK, often experiencing ongoing hardship and uncertainty regarding their legal status and housing situation. This study aimed to describe the UASCs population within social care and to describe the linkage between national social care and health data. Methods We used national, linked social care-hospital administrative data from the ECHILD database from 2005 to 2021. The UASC population was identified using the UASC flag which was already included in the social care dataset. We described the UASCs population and their linkage to health data, by gender, age group, ethnicity of placement type. Results The social care dataset included 38,8820 UASCs in the study period. The majority were male (86%, 33730/38820), aged 16 and over at placement (66%, 25530/38820). Only 1/10 were from a White ethnic background (10%, 3870/38820). Two in five UASCs were placed in foster care (care of strangers, 41%, 16000/38820), and half in unregulated and independent accommodations (50%, 10280/38820). Less than 22% of UASCs (8600/38820) in the social care dataset were linkable to hospital admission data. Linkage rates were lower for UASCs who were aged 16 and over (6%, 1480/25340), Black (17%, 1890/10910), or placed in unregulated and independent accommodations (11%, 1370/19270). Discussion This paper is the first to evaluate linkage between national social care and health data for UASCs in England. Less than a quarter of UASCs could be linked to health data. This reduces our capacity measure contact with health services for this vulnerable population of children.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".