The Gender-Diversity and Autism Questionnaire: A Community-Developed Clinical, Research, and Self-Advocacy Tool for Autistic Transgender and Gender-Diverse Young Adults
Bibliographic record
Abstract
Background: Autistic transgender people face unique risks in society, including inequities in accessing needed care and related mental health disparities. Given the need for specific and culturally responsive accommodations/supports, the characterization of key experiences, challenges, needs, and resilience factors within this population is imperative. This study developed a structured self-report tool for autistic transgender young adults to communicate their experiences and needs in a report format attuned to common autistic thinking and communication styles. Methods: This cross-nation project developed and refined the Gender-Diversity and Autism Questionnaire through an iterative community-based approach using Delphi panel methodology. This proof-of-principle project defined “expertise” broadly, employing a multi-input expert search approach to balance academic-, community-, and lived experience-based expertise. Results: The expert collaborators ( N = 24 respondents) completed a two-round Delphi study, which developed 85 mostly closed-ended items based on 90% consensus. Final item content falls within six topic areas: the experience of identities; the impact of experienced or anticipated discrimination, bias, and violence toward autistic people and transgender people; tasks and experiences of everyday life; gender diversity- or autism-related care needs and history; the experience of others doubting an individual's gender identity and/or autism; and the experience of community and connectedness. The majority of retained items relate to tasks and experiences of everyday life or the impact of experienced or anticipated discrimination, bias, and violence. Conclusions: This study employed a multipronged multimodal search approach to maximize equity in representation of the expert measure development team. The resulting instrument, designed for clinical, research, and self-advocacy applications, has parallel Dutch and English versions and is available for immediate use. Future cross-cultural research with this instrument could help identify contextual risk and resilience factors to better understand and address inequities faced by this large intersectional population.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".