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Record W2407358922 · doi:10.1080/02673037.2016.1181719

Toward an autism-friendly home environment

2016· article· en· W2407358922 on OpenAlexaffabout
Wasan Nagib, Allison Williams

Bibliographic record

VenueHousing Studies · 2016
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAutismPsychologyQuality (philosophy)Qualitative researchDevelopmental psychologySociologySocial science

Abstract

fetched live from OpenAlex

This study explores the challenges faced by children with autism and their families in the home environment and how physical elements of the home environment can be designed or modified to alleviate these challenges and create an autism-friendly home. The research employs qualitative methods to learn from the experiences of key informants involved in creating or modifying the home environment of people with autism; this involved interviews with architects and occupational therapists. To learn from the families themselves, an online survey of the families of children with autism across Canada and the United States was conducted. The study provides insight into the physical, social, and psychological challenges affecting the quality of life of children with autism and their families in their home environment and the contribution of home modifications to alleviating the challenges. The appropriateness of the three housing typologies – detached houses, attached houses, and apartments – to accommodate autism-related needs is discussed together with potential policy implications.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.004
Scholarly communication0.0030.003
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.184
GPT teacher head0.454
Teacher spread0.270 · 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

Citations78
Published2016
Admission routes2
Has abstractyes

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