Quality of life instruments for children and adolescents with neurodisabilities: how to choose the appropriate instrument
Bibliographic record
Abstract
AIM: There are many misconceptions about what constitutes 'quality of life' (QoL). It is often difficult for researchers and clinicians to determine which instruments will be most appropriate to their purpose. The aim of the current paper is to describe QoL instruments for children and adolescents with neurodisabilities against criteria that we think are important when choosing or developing a QoL instrument. METHOD: QoL instruments for children and adolescents with neurodisabilities were reviewed and described based on their purpose, conceptual focus, origin of domains and items, opportunity for self report, clarity (lack of ambiguity), potential threat to self-esteem, cognitive or emotional burden, number of items and time to complete, and psychometric properties. RESULTS: Several generic and condition-specific instruments were identified for administration to children and adolescents with neurodisabilities - cerebral palsy, epilepsy and spina bifida, and hydrocephalus. Many have parent-proxy and self-report versions and adequate reliability and validity. However, they were often developed with minimal involvement from families, focus on functioning rather than well-being, and have items that may produce emotional upset. INTERPRETATION: As well as ensuring that a QoL instrument has sound psychometric properties, researchers and clinicians should understand how an instrument's theoretical focus will have influenced domains, items, and scoring.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".