The Need for Sensory-Friendly “Zones”: Learning From Youth on the Autism Spectrum, Their Families, and Autistic Mentors Using a Participatory Approach
Bibliographic record
Abstract
Introduction Differences in sensory processing were linked to a diagnosis of autism spectrum disorder (ASD) before its inclusion as a core characteristic in the revised DSM-V. Yet, research focused on sensory processing and meaningful participation of children and youth with ASD remains relatively scarce. Although refinement of the International Classification of Functioning and Disability (ICF) relies on first-person accounts, longitudinal studies that foreground sensory experiences and its impact on involvement in a life situation from first-person perspectives are largely missing from this body of research. Objectives In this sub-study, we drew from a longitudinal participatory research project consisting of two separately funded studies with children and youth with ASD and their families between 2014 and 2021. The participatory project used photovoice (PV) methods to identify the primary concerns related to socio-spatial exclusion (PV-1) and the action steps needed to redress them (PV-2). The objective of this sub-study was to understand what really mattered to children with autism, their parents, autistic youth and an adult mentor to consider how their experiential knowledge could deepen understanding of meaningful participation. Materials and Methods We used an overarching narrative phenomenological and aesthetic theoretical framework to focus data analysis on the bodily sensing experiences related to significant moments or events, followed by an inductive thematic analysis of what mattered about those moments. Results The topical areas of concern that emerged from analyses were: (1) the relationship between sensory experiences and mental health ( motion madness ); (2) the indivisibility or layering of sensory and social experiences ( squishing and squeezing ); (3) the impact when “tricks” to stay involved are categorically misunderstood ( When you don’t respond in the correct way ), and (4) how care and consideration of others can lead to innovative solutions for inclusion ( I can’t be the only one ). Listening to the bodily-sensing experiences of children with ASD, autistic youth and adults, and their families in their own terms has implications for remapping the ICF and envisioning sensory curb-cuts to access, initiate and sustain occupational participation for all.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".