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Record W2935920591 · doi:10.5206/eei.v29i1.7777

Using the ICF-CY to Support Inclusive Education in Ghana

2019· article· en· W2935920591 on OpenAlexaffvenue
Christiana Okyere, Catherine Donnelly, Heather M. Aldersey

Bibliographic record

VenueExceptionality Education International · 2019
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsInternational Classification of Functioning, Disability and HealthSpecial educationInclusion (mineral)PsychologyIntellectual disabilityMedical educationTypically developingDevelopmental psychologyPedagogyMedicineSocial psychologyAutism

Abstract

fetched live from OpenAlex

The international classification of functioning, disability, and health for children and youth (ICF-CY) developed by the World Health Organization (WHO) is a framework for understanding concepts of disability specific to children and youth. This framework has been used in countries around the world to support the education of children with disabilities. In this article, we argue that the ICF-CY has the potential to inform and support Ghana’s education system and to improve the implementation of education for children with disabilities, particularly inclusive education, in Ghana. Specifically, we use children with intellectual and developmental disabilities (IDD) as an exemplar to examine how the ICF-CY can support inclusive education for children with disabilities within its main components: Body Functions and Structures, Activities and Participation, Environmental Factors, and Personal Factors. Examining the ICF-CY in these areas is significant, as many similar low- and middle-income contexts have yet to adopt the framework and may draw insights and lessons for its significance in educational contexts.

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)0.006
metaresearch head score (Gemma)0.011
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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0020.002
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.064
GPT teacher head0.480
Teacher spread0.416 · 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
Published2019
Admission routes2
Has abstractyes

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