A step-by-step and data-driven guide to index gender in psychiatry
Bibliographic record
Abstract
Beyond sex as a binary or biological variable, within-sex variations related to sociocultural gender variables are of increasing interest in psychiatric research to better understand individual differences. Using a data-driven approach, we developed a composite gender score based on sociodemographic and psychosocial variables showing sex differences in a sample of psychiatric emergency patients upon admission (N = 1708; 39.4% birth-assigned females; mean age = 40 years; age standard deviation = 14). This gender score was extracted from a confirmatory factor analysis (CFI = 0.966; RMSEA = 0.044, SRMR = 0.030) and could predict a person's birth-assigned sex with 67% accuracy. This score allowed the further identification of differences on impulsivity measures that were absent when looking solely at birth-assigned sex. Female birth-assigned sex was also associated with higher rates of mood and personality disorder diagnoses, while higher feminine gender scores were related to higher proportions of anxiety and mood disorder diagnoses. By contrast, male birth-assigned sex and higher masculine gender scores were associated with higher proportions of psychotic and substance use disorder diagnoses. Patients with undifferentiated gender scores (i.e., scoring between masculine and feminine threshold defined by terciles) were more represented in the psychotic disorder group. Considering both sex and gender in psychiatric research is essential and can be achieved even when using secondary data to index gender comprised of demographic and psychosocial variables.
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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 | 0.026 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.074 | 0.056 |
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".