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Quality of life instruments for children and adolescents with neurodisabilities: how to choose the appropriate instrument

2009· review· en· W1973044606 on OpenAlexaff
Elizabeth Waters, Elise Davis, Gabriel M. Ronen, Peter Rosenbaum, Michael Livingston, Saroj Saigal

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2009
Typereview
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsWestern UniversityMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsCerebral palsyPsychologyQuality of life (healthcare)CLARITYCognitionProxy (statistics)PsychometricsClinical psychologyReliability (semiconductor)Applied psychologyDevelopmental psychologyMedicinePsychiatryPsychotherapistComputer science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.297
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations158
Published2009
Admission routes1
Has abstractyes

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