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Exploration of the link between conceptual occupational therapy models and the International Classification of Functioning, Disability and Health

2005· article· en· W2063975835 on OpenAlexaboutno aff
Tanja Stamm, Alarcos Cieza, Klaus Machold, Josef S Smolen, Gerold Stucki

Bibliographic record

VenueAustralian Occupational Therapy Journal · 2005
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsInternational Classification of Functioning, Disability and HealthOccupational therapyConceptual modelMultidisciplinary approachContext (archaeology)Occupational sciencePerspective (graphical)Conceptual frameworkActivities of daily livingPsychologyRehabilitationMedicinePhysical therapyComputer scienceArtificial intelligenceSociologySocial science

Abstract

fetched live from OpenAlex

Background and Aim: Because occupational therapy focuses on occupations and activities of daily living in the context of the environment, conceptual occupational therapy models might be closely related to the International Classification of Functioning, Disability and Health (ICF). The purpose of this paper is to explore the link of conceptual occupational therapy models to the ICF. Methods and Results: A structured literature search was performed. The concepts on which the models are built were linked to the ICF categories and components according to 10 established linking rules. Three conceptual occupational therapy models were identified in the literature: the Model of Human Occupation, the Canadian Model of Occupational Performance and the Occupational Performance Model (Australia). The majority of the concepts from the three models could be linked to the ICF. Conclusion: By applying the conceptual models, occupational therapists might add an additional perspective to multidisciplinary teams that use the ICF.

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)0.019
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.068
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.009
Science and technology studies0.0020.006
Scholarly communication0.0060.007
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.400
Teacher spread0.131 · 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 designTheoretical or conceptual
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

Citations95
Published2005
Admission routes1
Has abstractyes

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