Educational outcomes of children in Wales with cerebral palsy
Bibliographic record
Abstract
Background with rationaleIt is recognized that children with disability have special educational needs (SEN). They are likely to have poor school attendance and do not achieve well academically. Many children with a cerebral palsy (CP) have SEN but little is known about their educational provision or outcomes. Main AimTo investigate the educational experience of children in Wales with CP and describe the type of SEN and SEN provision; school attendance; achievement—teacher assessments at the end of the Foundation Phase and Key Stages 2 and 3 of the National Curriculum (NC)—and in General Certificate of Secondary Education (GCSE) examinations. Methods/ApproachData from the Pupil Level Annual School Census (PLASC), NC and GCSE results from 2009 to 2016 were linked with routine e-health records of primary and secondary health care data held in SAIL. The health care records were used to identify pupils who, potentially, had a cerebral palsy. ResultsThe linked data set included around 360,000 pupils per school census of whom 1200–1400 per school census were identified as having a CP, representing a crude prevalence of 0.4%. Adjusted for age, year and sex, there was no significant variation in prevalence by area deprivation. Around 60% of children with a CP have a statement of SEN; over a quarter of CP children are educated in special schools; CP children in mainstream (primary, middle and secondary) schools tended to miss more school sessions (~50% more) than other children and lower percentages achieved the expected levels at Key Stages 2 and 3 and the Level 2 GCSE threshold. Conclusion/Implications This work demonstrates the utility of record-linkage between health and education data to map, monitor and provide information to parents, carers and policymakers about education outcomes for this group of children to inform planning and service provision.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".