“I am afraid of being treated badly if I show it”: A cross-sectional study of healthcare accessibility and Autism Health Passports among UK Autistic adults
Bibliographic record
Abstract
BACKGROUND: Autistic people are more likely to experience stigma, communication barriers and anxiety during healthcare. Autism Health Passports (AHPs) are a communication tool that aim to provide information about healthcare needs in a standardised way. They are recommended in research and policy to improve healthcare quality. AIM: To explore views and experiences of AHPs among Autistic people from the UK who have been pregnant. METHODS: We developed an online survey using a combination of open and closed questions focused on healthcare impairments and views and experiences of AHPs. Data were anlaysed using descriptive statistics, Kruskal-Wallis tests, and content analysis. FINDINGS: Of 193 Autistic respondents (54% diagnosed, 22% undergoing diagnosis and 24% self-identifying), over 80% reported anxiety and masking during healthcare always or most of the time. Some significant differences were identified in healthcare (in)accessibility by diagnostic status. Only 4% of participants knew a lot about AHPs, with 1.5% of participants using one at least half of the time. Almost three quarters of respondents had not previously seen an AHP. Open text responses indicated that the biggest barrier to using an AHP was a belief that health professionals would discriminate against Autistic patients. Additional barriers included staff lack of familiarity with AHPs and respondents expecting a negative response to producing an AHP. CONCLUSIONS: Our findings suggest that AHPs are not reducing health inequalities for Autistic adults who have been pregnant. Alternative solutions are needed to reduce health inequalities for Autistic people.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".