Beyond the Binary: Gender Inclusivity in Schizophrenia Research
Bibliographic record
Abstract
Schizophrenia is a severe neuropsychiatric disorder with significant differences in the incidence and symptomology between cisgender men and women. In recent years, considerably more attention has been on the inclusion of sex and gender in schizophrenia research. However, the majority of this research has failed to consider gender outside of the socially constructed binary of men and women. As a result, little is known about schizophrenia in transgender and gender-nonconforming populations. In this review, we present evidence showing that transgender and gender-nonconforming individuals have elevated risk of developing schizophrenia, and we discuss minority stress theory and other potential factors that may contribute to this risk. The need for inclusion of transgender and gender-nonconforming communities in schizophrenia research is emphasized, alongside a discussion on considerations and challenges associated with this type of research. Finally, we offer specific strategies to make research on schizophrenia, and research on other neuropsychiatric disorders, more inclusive of those populations that do not fall within the socially constructed gender binary. If we are to succeed in the development of more personalized therapeutic approaches for all, a better understanding of the variability of the human brain is needed.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".