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Record W2104627637 · doi:10.2182/cjot.2010.77.5.5

Content Validation of the International Classification of Functioning, Disability and Health (ICF) Core Set for Stroke: The Perspective of Occupational Therapists

2010· article· en· W2104627637 on OpenAlexvenueno aff
Andrea Gläßel, Inge Kirchberger, Elisabeth Linseisen, Tanja Stamm, Alarcos Cieza, Gerold Stucki

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2010
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsInternational Classification of Functioning, Disability and HealthPerspective (graphical)Occupational therapyCore (optical fiber)Stroke (engine)Physical medicine and rehabilitationSet (abstract data type)PsychologyOccupational sciencePhysical therapyContent validityMedicineRehabilitationClinical psychologyPsychometricsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: The "ICF Core Set for stroke" is an application of the International Classification of Functioning, Disability and Health (ICF) and represents the typical spectrum of problems in the functioning of patients with stroke. PURPOSE: The objective of this study was to validate this ICF Core Set from the perspective of occupational therapists. METHODS: In a three-round electronic mail survey using the Delphi technique, occupational therapists experienced in stroke treatment were asked about patients'problems, patients' resources, and aspects of environment they take care of. Two health professionals linked responses to the ICF. FINDINGS: Sixty-nine occupational therapists in 21 countries named 1,747 concepts that occupational therapists treat in patients with stroke. These concepts were linked to 347 different ICF categories. Twenty-three concepts were linked to the ICF component Personal Factors. CONCLUSION: The content validity of the "ICF Core Set for stroke" was largely supported by occupational therapists.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.270
GPT teacher head0.414
Teacher spread0.144 · 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.

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

Citations33
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Occupational TherapySame topicCerebral Palsy and Movement DisordersFrench-language works237,207