A trial of the AASPIRE healthcare toolkit with Australian adults on the autism spectrum
Bibliographic record
Abstract
BACKGROUND: Autistic adults experience barriers to accessing health care, such as service provider communication not meeting their needs, healthcare facilities causing sensory discomfort and feeling fear or anxiety regarding their healthcare visit. The Academic Autism Spectrum Partnership in Research and Education (AASPIRE) developed and trialled an online healthcare toolkit to reduce such barriers and improve healthcare interactions between autistic adults and their primary care providers in the United States. This preliminary study aimed to explore experiences of autistic adults using the AASPIRE Healthcare Toolkit in Australia. METHODS: Semi-structured interviews were conducted with six autistic adults about their experiences and perceptions of utilising the toolkit in an Australian healthcare setting. RESULTS: Participants identified that the toolkit facilitated their interactions with health professionals by providing structure to appointments, supplementing new knowledge and increasing individual confidence. They also offered suggestions to tailor the toolkit for use in Australia. CONCLUSIONS: Future research should seek to explore the experiences of autistic adults using a version of the toolkit adapted for Australian use, as well as exploring the views of health professionals utilising it.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".