Exploring the Self-Diagnosis Experiences of Self-Diagnosed Autistic Women
Bibliographic record
Abstract
BACKGROUND: Many Autistic women are misdiagnosed, diagnosed as Autistic later in life, or not identified or diagnosed at all. Some women choose to self-diagnose as Autistic, yet their experiences have not been explored in depth in Autism literature. This qualitative study explored the self-diagnosis experiences of Autistic women. METHODS: Seven people living in Ontario were asked about their self-diagnosis experiences through either a virtual interview or an open-ended questionnaire. Drawing on reflexive thematic analysis procedures, data was analyzed using critical Autism studies as a theoretical lens. RESULTS: Self-diagnosis aims were described as a way to find answers through professionals, the internet, and people in their lives. Self-diagnosis processes were described as increasing one’s self-acceptance, allowing one to sense-make life experiences, and make connections to Autistic communities. IMPLICATIONS: This research highlights the need for neurodiversity-affirming training for health professionals and the importance of Autistic communities for the wellbeing of Autistic people.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".