Quality of Life and Behavioural Adjustment in Childhood Hydrocephalus
Bibliographic record
Abstract
The aim of the paper is to describe parent and teacher reported behavioural outcomes and quality of life in childhood hydrocephalus, and to consider the implications for future service planning. A community sample of 235 school-aged children with hydrocephalus (5-16 years) were identified via a database of service users, held by the Scottish Spina Bifida Association. Parent and teacher reports of behaviour on the Strengths and Difficulties Questionnaire (SDQ), and parent reports of quality of life on the Paediatric Quality-of-Life Generic Core (PedsQL Core) and Paediatric Quality-of-Life Fatigue (PedsQL Fatigue) were obtained, as were reports of service use and satisfaction. In total, 35% (n = 76) of parents and 86% (n = 47) of teachers who were contacted participated in the study. Parents reported behavioural difficulties in 57% and teachers in 33% of children. Quality of life was significantly reduced in comparison to published norms. Children whose parents reported unmet needs had poorer psychosocial outcomes, but families rarely accessed appropriate specialist services. In conclusion, hydrocephalus is associated with high rates of behaviour problems and markedly reduced quality of life. It is important to increase professional awareness of psychological need in this chronic neurological condition, and to increase access to appropriate psychosocial services.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".