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Record W4387819714 · doi:10.5430/jct.v12n6p60

Identification of Social Needs among Students with Disabilities from the View Point of Their Teachers: UAE Perspective

2023· article· en· W4387819714 on OpenAlexvenueno aff
Mohamad Salman Alkhazaleh, Shirin S. AlOdwan, Samer Abdel Hadi, Reema Al-Qaruty, Bilal Fayiz Obeidat‎

Bibliographic record

VenueJournal of Curriculum and Teaching · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychology of Development and Education
Canadian institutionsnot available
Fundersnot available
KeywordsAbu dhabiContext (archaeology)Perspective (graphical)PsychologyIdentification (biology)SituatedPoint (geometry)Social needsInclusion (mineral)Developmental psychologySocial psychologyMedical educationSociologyMedicinePolitical scienceGeography

Abstract

fetched live from OpenAlex

The goal of this research endeavor was to discern the social requirements of children with disabilities within the educational context of schools situated in the emirate of Abu Dhabi. Additionally, the study sought to explore potential differentials in the extent of these needs predicated upon variables such as gender and age. To this end, the researchers used a questionnaire consisting of 17 distinct items was methodically constructed and subsequently administered to a demographically diverse cohort of 83 educators, encompassing both male and female participants. The empirical findings of this investigation underscored the considerable significance of addressing the social needs encountered by children with disabilities in the Emirates of Abu Dhabi's educational institutions. Furthermore, the research focused on disparities in social needs were not significantly contingent upon gender-based distinctions. However, the analysis shows that age emerged as a substantively influential variable. Specifically, the researchers found that children within the age range of 7 to 10 exhibited a greater degree of social needs in comparison to their counterparts belonging to other age brackets.

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 score0.023
Threshold uncertainty score0.231

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 head0.022
GPT teacher head0.346
Teacher spread0.323 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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