Assuming ability of youth with autism: Synthesis of methods capturing the first-person perspectives of children and youth with disabilities
Bibliographic record
Abstract
Most research regarding youth with autism spectrum disorder has not focused on their first-person perspectives providing limited insight into methodologies best suited to eliciting their voices. We conducted a synthesis of methods previously used to obtain the first-person perspectives of youth with various disabilities, which may be applicable to youth with autism spectrum disorder. Two-hundred and eighty-four articles met the inclusion criteria of our scoping review. We identified six distinct primary methods (questionnaires, interviews, group discussion, narratives, diaries, and art) expressed through four communication output modalities (language, sign language and gestures, writing, and images). A group of parents who have children with autism spectrum disorder were then presented with a synthesis of results. This parent consultation was used to build on approaches identified in the literature. Parents identified barriers that may be encountered during participant engagement and provided insights on how best to conduct first-person research with youth with autism spectrum disorder. Based on our findings, we present a novel methodological framework to capture the perspectives of youth with various communication and cognitive abilities, while highlighting family, youth, and expert contributions.
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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 | 0.068 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".