Roadblocks and detours on pathways to a clinical diagnosis of autism for girls and women: A qualitative secondary analysis
Bibliographic record
Abstract
BACKGROUND: Autism is not always considered for girls and women until later along their clinical diagnostic pathways. Misdiagnosis or late diagnosis can pose significant disadvantages with respect to accessing timely health and autism-related services and supports. Understanding what contributes to roadblocks and detours along clinical pathways to an autism diagnosis can shed light on missed opportunities for earlier recognition. OBJECTIVE: Our objective was to examine what contributed to roadblocks, detours, and missed opportunities for earlier recognition and clinical diagnosis of autism for girls and women. DESIGN: We conducted a qualitative secondary analysis using data from a Canadian primary study that examined the health and healthcare experiences of autistic girls and women through interviews and focus groups. METHODS: Transcript data of 22 girls and women clinically diagnosed with autism and 15 parents were analysed, drawing on reflexive thematic analysis procedures. Techniques included coding data both inductively based on descriptions of roadblocks and detours and deductively based on conceptualizations of sex and gender. Patterns of ideas were categorized into themes and the 'story' of each theme was refined through writing and discussing analytic memos, reflecting on sex and gender assumptions, and creating a visual map of clinical pathways. RESULTS: Contributing factors to roadblocks, detours, and missed opportunities for earlier recognition and diagnosis were categorized as follows: (1) age of pre-diagnosis 'red flags' and 'signals'; (2) 'non-autism' mental health diagnoses first; (3) narrow understandings of autism based on male stereotypes; and (4) unavailable and unaffordable diagnostic services. CONCLUSION: Professionals providing developmental, mental health, educational, and/or employment supports can be more attuned to nuanced autism presentations. Research in collaboration with autistic girls and women and their childhood caregivers can help to identify examples of nuanced autistic features and how context plays a role in how these are experienced and navigated.
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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".