Research methods at the intersection of gender diversity and autism: A scoping review
Bibliographic record
Abstract
Research has increasingly focused on the intersection between gender diversity and autism. Understanding the foci, methods, and rigor of recent studies could help guide and maximize impact in this accelerating research area. We conducted a scoping review of peer-reviewed literature on this topic published between 2018 and 2023. The search generated 1432 records after deduplication. Included studies ( N = 84) were of English language, featured original qualitative or quantitative findings, and examined a psychosocial connection between autism and gender spectra variables. Autism prevalence among gender-diverse people was the most-studied sub-topic. Methodological rigor was acceptable overall; however, we identified recurrent threats to generalizability and validity, including inconsistent conceptualization of constructs (e.g. gender dysphoria), weak participant sampling and characterization, and reliance on unvalidated measures. Addressing these limitations and meaningfully engaging with community shareholders will be critical to enhancing the replicability and clinical impact of future research. Lay Abstract Research has increasingly focused on the intersection between gender diversity and autism. To better understand this literature, this scoping review systematically searched five databases for peer-reviewed literature on gender diversity and autism published between 2018 and 2023. Included studies ( N = 84) were of English language, featured original qualitative or quantitative findings, and examined a psychosocial connection between autism and gender spectra variables. Most studies focused on measuring prevalence of autism among gender-diverse individuals. While the overall study rigor was acceptable, weaknesses in measurement, sample selection, and definition of key terms were noted. Promisingly, studies in this area appear to be shifting away from a pathologizing lens and towards research methods that engage in meaningful collaboration with the autistic, gender-diverse community to investigate how to best enhance the quality of life and wellbeing of this 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".