How do autistic adults experience ageing? A qualitative interview study
Bibliographic record
Abstract
BackgroundDespite increasing numbers of people aging with autism, little is known about the ageing experiences and support needs of middle-aged and older autistic adults. This is important, especially as autistic people have higher rates of health conditions, decreased functional independence, and experience greater difficulties in accessing relevant support, in comparison to their non-autistic counterparts. The aim of this study was therefore to understand middle-aged and older autistic adults’ views and experiences of ageing well.MethodSeventeen autistic adults (10 women and seven men), aged from 46 to 72 years (mean age=56 years) were interviewed about their understanding of what it means to age well; their age-related needs; and how services could better support them to age well. The transcribed interviews were analysed using inductive thematic analysis. ResultsThe findings revealed several ways that autism influenced people’s ability to age well. This included concerns about a perceived higher likelihood of age-related conditions such as dementia; age-related changes in the experiences of autistic characteristics; a lack of knowledge and understanding about autism and ageing; increased risks of social isolation and inadequate support system; and a lack of appropriate support services. ConclusionNovel recommendations for supporting autistic adults to age well were identified, including involving autistic people in the design of health and social care services, supporting ageing autistic adults with reablement; promoting their autonomy and individual strengths and introducing specialised advocate/coordinator roles or “one stop-shop hubs” that could help autistic people further their understanding of the interactions between ageing and autism.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".