Return to school after acquired brain injury in the UK – the educators' perspectives
Bibliographic record
Abstract
Childhood acquired brain injury (ABI) is associated with poorer life outcomes. Increasing numbers of children and young people are surviving severe brain injury and returning to mainstream schools with multiple impairments. It is widely acknowledged that for these children, their school becomes by default their rehabilitation centre. International studies of this transition and a recent UK government report criticize educators' inconsistent implementation of support strategies, lack of educator training and poor communication between clinicians, educators, child and family. The educators' perspectives of the return‐to‐school are, however, not well represented in the literature. This study therefore explored the experiences of educators in the UK (N = 10) who had recently facilitated a return‐to‐school of a child with ABI aged 8–12 (N = 5) using semi‐structured interviews analysed by data‐driven thematic analysis. The findings highlight common experiences: a continuum of intensive problem‐solving with heavy reliance on the Special Educational Needs Coordinator; educators valuing collaboration with clinical specialists in context over general ‘training’; uncertainty over the validity of implementing support strategies from prior teaching experience; uncertainty about how to support the child's emotional needs; and frustration with UK statutory processes for Special Educational Needs and Disabilities. Recommendations are made for changes to practice and future research.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".