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Record W4401921452 · doi:10.1192/j.eurpsy.2024.272

Guidelines of inclusive architecture design for autism spectrum disorder: What is new?

2024· article· en· W4401921452 on OpenAlexaboutno aff
E. Abdelmoula, N. Bouayed Abdelmoula, B. Abdelmoula

Bibliographic record

VenueEuropean Psychiatry · 2024
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
Fundersnot available
KeywordsAutism spectrum disorderArchitectureSpectrum (functional analysis)PsychologyAutismComputer architectureComputer scienceDevelopmental psychologyPhysicsGeography

Abstract

fetched live from OpenAlex

Introduction Autism spectrum disorder (ASD) is a complex neuro-developmental condition. According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), restricted interests and repetitive behaviors and difficulties with social communication and interaction characterize ASD. Different ways of learning, moving, or paying attention are related to the degree of impairments. By reducing environmental and social obstacles in school, work, and other areas of life, architecture could play a pivotal role in helping people on the spectrum become more independent and acquire more abilities. Objectives The aim of this study was to outline the recommendations and guidelines of the inclusive architecture design for ASD. Methods We conducted a comprehensive review of the scientific literature using the following keywords: inclusive design, architecture, autism or ADS. Results Our research found that the Autism ASPECTSS design index reported in 2013 by Magda Mostafa from Canada, which was based on the sensory design theory, is the world’s first set of evidence-based design guidelines for managing built environments to serve ADS individuals interaction, particularly in schools and workspaces. ASPECTSS conceptual framework delineate seven design concepts: acoustics, spatial sequencing, escape space, compartmentalization, transition spaces, sensory zoning, and safety. In 2023, the same author published an autism friendly design guide for the world’s first autism-friendly university. This guide is characterized by a better understanding of human-centered design and advocates beyond the mere inclusion, aspiring to a state where the boundaries between ‘normal’ and ‘special’ are blurred in order to treat all users as human beings with equal rights, thus calling for equal opportunities beyond the ADS spectrum. Conclusions With such well-established conceptual framework, it is nowadays imperative to expand our buildings in cities, schools, workplaces, hospitals, and public areas using the guidelines of autism-friendly environments. These buildings will enhance our individual and social well-being. Disclosure of Interest None Declared

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)0.039
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.098
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0100.005
Science and technology studies0.0040.008
Scholarly communication0.0080.008
Open science0.0070.006
Research integrity0.0090.009
Insufficient payload (model declined to judge)0.0030.003

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 head0.072
GPT teacher head0.438
Teacher spread0.366 · 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 designNot applicable
Domainnot available
GenreMethods

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".

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Citations1
Published2024
Admission routes1
Has abstractyes

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