Supporting autistic adults with complex communication needs in making their voices heard: examining an adaptation of the Autism Voices framework
Bibliographic record
Abstract
Introduction Autistic adults with speech, language and/or cognitive challenges are often excluded from research, particularly from studies examining first-person perspectives, as these generally require that participants have strong speech, language, and cognitive skills. The current pilot study extends previous work and examines whether the Autism Voices framework can be adapted for use with a pre-existing interview the Camberwell Assessment of Need for Adults with Developmental and Intellectual Disabilities - Research version (CANDID-R). Methods Eleven young autistic adults with complex communication needs completed the CANDID-R interview using visual supports. These visual supports were provided to assist participants’ comprehension of interview questions and to support them in answering the interview questions. Participants’ caregivers also completed the interview and their answers to specific validation questions were compared to those of their adult children. Additionally, behavioral observations were also completed. Results The findings from this pilot study indicate that our adaptation of the Autism Voices framework was, at least partially successful in supporting participants in answering the interview questions. Additionally, behavioral observations indicate that the visual supports helped participants remain engaged throughout the interview. However, results also indicate that further adaptations, which we discuss, will be required. Conclusion Autistic people with complex communication needs must be included in research about the lived experiences of autistic people. Building on previous work, we show that, with dedication and imagination, equitable and inclusive research is possible.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".