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Record W2334326192 · doi:10.1177/000841740707405s06

A Comparison of the International Classification of Functioning, Disability, and Health to the Disability Tax Credit

2007· article· en· W2334326192 on OpenAlexaffvenue
Angela Conti‐Becker, Samantha Doralp, Nora Fayed, Crystal O. Kean, Raphael Lencucha, Rhysa Leyshon, Jackie Mersich, Shawn Robbins

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2007
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsInternational Classification of Functioning, Disability and HealthCertificationProcess (computing)Disability benefitsPsychologyActivities of daily livingInclusion (mineral)Physical medicine and rehabilitationMedicinePhysical therapyComputer scienceRehabilitationPolitical scienceSocial psychologySocial security

Abstract

fetched live from OpenAlex

BACKGROUND: The Disability Tax Credit (DTC) Certification is an assessment tool used to provide Canadians with disability tax relief The International Classification of Functioning, Disability and Health (ICF) provides a universal framework for defining disability. PURPOSE: The purpose of this study was to evaluate the DTC and familiarize occupational therapists with the process of mapping measures to the ICF classification system. METHOD: Concepts within the DTC were identified and mapped to appropriate ICF codes (Cieza et al., 2005). RESULTS: The DTC was linked to 45 unique ICF codes (16 Body Functions, 19 Activities and Participation, and 8 Environmental Factors). IMPLICATIONS: The DTC encompasses various domains of the ICF; however, there is no consideration of Personal Factors, Body Structures, and key aspects of Activities and Participation. Refining the DTC to address these aspects will provide an opportunity for fair and just determinations for those who experience disability.

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)0.014
metaresearch head score (Gemma)0.054
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.664

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.054
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.009
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.178
GPT teacher head0.430
Teacher spread0.252 · 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

Citations10
Published2007
Admission routes2
Has abstractyes

Explore more

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