Bibliographic record
Abstract
Summary: This study examined national trends in how children with disabilities and mental health concerns are treated within children's social care in England. Using a 100% sample of data from the Annual Children in Need Census (2015–2023), obtained through Freedom of Information requests, the analysis was guided by an evidence-based policy framework. The focus was on assessments, child protection investigations, and the categorization of children's primary needs to understand how practice has shifted over time. Findings: The results show a 77.1% increase in assessments identifying concerns about disability or mental health, which now account for a quarter of all assessments. Section 47 child protection investigations for these children rose by 145.2%, compared with a 45.4% increase for other children. Meanwhile, the proportion recorded with “disability or illness” as their primary need fell by 17.4%. These findings indicate a declining focus on addressing the specific needs of disabled children and a rising emphasis on risk. This pattern reflects concerns raised by parent-led groups and prior research that families are often viewed with suspicion, leading to “parent blame” and intrusive interventions rather than supportive services. Applications: The study highlights the need for policy and practice change to ensure disabled children receive appropriate support. Recommended actions include separating assessment of need from child protection investigations, requiring practitioners to develop disability expertise, creating a national strategy to reduce over-reliance on investigative approaches, and properly funding child-in-need services. These steps would help re-balance the system toward meeting needs rather than blaming parents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".