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Record W4308183720 · doi:10.1177/13623613221132108

Autism voices: Perspectives of the needs, challenges, and hopes for the future of autistic youth

2022· article· en· W4308183720 on OpenAlexafffund
Rackeb Tesfaye, Valérie Courchesne, Pat Mirenda, Wendy Mitchell, David Nicholas, Ilina Singh, Lonnie Zwaigenbaum, Mayada Elsabbagh

Bibliographic record

VenueAutism · 2022
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of AlbertaUniversity of CalgaryCentre for Addiction and Mental HealthUniversity of British ColumbiaMcGill University
FundersCentre Azrieli de recherche sur l'autisme, Institut et Hôpital Neurologiques de MontréalFonds de Recherche du Québec - SantéStollery Children’s Hospital FoundationWellcome TrustCanadian Institutes of Health ResearchChildren Neurodevelopmental Disorders Network
KeywordsAutismPsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

The Autism Voices study draws on novel inclusive methods to obtain the first-person experiences of autistic youth with a range of cognitive and verbal abilities. Thirty-one autistic youth were interviewed with a strength-based protocol, enabling them to provide responses in the modality of their choice. Dynamics between youth and their environments such as home, school, and community were explored. Youth were questioned about their interests, plans for the future, experiences with various emotions, and experience of autism. Based on a thematic analysis, six themes emerged: (1) autistic identities, (2) thinking about the future, (3) seeking social connection on their own terms, (4) seeking autonomy, (5) school as both a stressor and social facilitator, and (6) experiences of stress and anxiety. The experiences described by autistic youth parallel many of the aspirations and challenges of typically developing adolescents, while being uniquely shaped by their autism. We discuss how these insights shared by autistic youth can facilitate active involvement in their communities, promote well-being, and promote optimal transition into adulthood. Autism Voices demonstrates that partnering with multiple stakeholders and the use of inclusive methodologies are pivotal steps toward capturing the voices of all autistic youth. Lay abstract Currently, our understanding of the adolescent period for autistic youth has relied on the expertise of researchers, clinicians, parents, and teachers, yet rarely involves their unique first-person experiences. Our study attempted to understand the experiences and perspectives of autistic adolescents in their home, school, and community environments using the Autism Voices protocol, a semi-structured interview specifically designed and tailored to engage with autistic youth with various language and intellectual levels. The analysis of the 31 interviews conducted with autistic adolescents aged 11–18 years highlighted six themes: (1) autistic identities, (2) thinking about the future, (3) seeking social connection on their own terms, (4) seeking autonomy, (5) school as both a stressor and social facilitator, and (6) experiences of stress and anxiety. These results highlight similarities and differences in the adolescent experiences of autistic youth compared to their typically developing peers. Our findings suggest that by removing assumptions about the experiences of autistic individuals and investing in inclusive interview methods, we can faithfully capture the experiences of autistic youth regardless of their communication and cognitive abilities. Being able to capture and amplify these diverse voices will facilitate the active involvement of autistic communities in research and clinical and policy decisions that impact them.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0110.007
Scholarly communication0.0070.005
Open science0.0010.008
Research integrity0.0020.004
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 head0.038
GPT teacher head0.277
Teacher spread0.239 · 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 designQualitative
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

Citations53
Published2022
Admission routes2
Has abstractyes

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