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Record W2761200597 · doi:10.1093/pch/20.5.e41

23: Which Measure Should I Use?: Content Analysis Using the ICF Core Sets for Children and Youth with Cerebral Palsy

2015· article· en· W2761200597 on OpenAlexaff
Verónica Schiariti, Karen Sauve, Sandy K. Tatla

Bibliographic record

VenuePaediatrics & Child Health · 2015
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInternational Classification of Functioning, Disability and HealthContent validityCerebral palsyPsychologyObservational studyConstruct validityClinical psychologyInclusion (mineral)Intervention (counseling)Reliability (semiconductor)Physical therapyMedicinePsychometricsRehabilitationPsychiatrySocial psychologyPathology

Abstract

fetched live from OpenAlex

Selecting appropriate measure(s) for clinical and/or research applications for children and youth with Cerebral Palsy (CP) poses many challenges. The newly developed International Classification of Functioning (ICF) Core Sets for children and youth with CP serve as universal guidelines for assessment, intervention and follow up. Importantly, the ICF Core Sets can guide professionals in selecting the most appropriate outcome measures to comprehensively capture information regarding children and youth with CP. To identify valid and reliable measures used with children and youth with CP, and to characterize the content of each measure using the ICF Core Sets for children and youth with CP as a framework. A systematic review of the literature was completed using multiple search engines likely to capture studies involving children with CP published between 1998 and 2013. Inclusion criteria consisted of: studies on children and/or youth with CP and interventional or observational studies published in English. All clearly defined outcome measures used in the studies were retrieved. Measures were classified as discriminative, predictive and evaluative. Psychometric properties were extracted when available. Construct of the measures identified in studies were linked to the ICF by two trained professionals. Subsequently, the content of each multiple-item measure (i.e. questionnaires) was analysed using the ICF Core Sets for children and youth with CP as a reference. Descriptive analysis was conducted in SPSS and content comparison was performed in Excel. Overall, 233 studies met inclusion criteria that described 80 multiple-item measures. Of these, 57 measures (72%) included reliability and validity testing. The majority of the measures were discriminative, generic and designed for school-aged children. Measures with proven psychometric properties contained considerable variability in the degree to which their content represented the ICF Core Sets for children and youth with CP. Primarily, measures covered the ICF components of body functions and activities and participation. Mental functions, mobility, and self-care were the most frequent areas represented by the measures. Overall, measures reflected few categories comprising the ICF Core Sets, ranging between 2% to 44% depending on the type of Core Set (comprehensive versus brief Core Set). A single measure covered the majority of the environmental factors included in the ICF Core Sets. Results from this content analysis provide novel information by applying the ICF Core Sets to characterize measures used with children and youth with CP. Few measures include items comprising the ICF Core Sets. As such, a combination of measures is needed to provide a comprehensive representation of the relevant areas of functioning included in the ICF Core Sets. Our results will guide professionals seeking appropriate measures to meet their research and clinical needs.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.051
metaresearch head score (Gemma)0.142
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.142
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0130.014
Science and technology studies0.0020.002
Scholarly communication0.0060.006
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.131
GPT teacher head0.326
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2015
Admission routes1
Has abstractyes

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