Sex versus gender associations with depressive symptom trajectories over 24 months in first-episode schizophrenia spectrum disorders
Bibliographic record
Abstract
BACKGROUND: Females with schizophrenia often experience more severe and persistent depressive symptoms than males, in particular during the acute phase of the illness. In contrast to sex (a biological distinction), little is known about the associations between gender (a societal construct) and depression in schizophrenia. AIM: We examined the associations of sex versus gender with visit-wise changes in depressive symptoms over 24 months in patients with first-episode schizophrenia spectrum disorders (FES) (n = 77) compared to matched healthy controls (n = 64). METHODS: The Bem Sex Role Inventory was used to measure feminine gender role endorsement. The Calgary Depression Scale for Schizophrenia was used to measure depressive symptoms at baseline, weeks 2, 4, and 6, and months 3, 6, 9, 12, 15, 18, 21, and 24. We used mixed models for continuous repeated measures to examine the moderating effects of childhood trauma, premorbid adjustment, age of psychosis onset, and cannabis use on the associations of sex and gender with depressive symptoms. RESULTS: Higher feminine gender role endorsement, independent of biological sex, was associated with more severe baseline depression and worse initial treatment trajectories. Childhood trauma exposure was also associated with worse depression outcomes, and mediated the association between gender and pre-treatment depression severities. CONCLUSIONS: Gender, but not sex, was associated with depressive symptom trajectories in FES. The consideration of both sex and gender offered a more nuanced insight into depressive symptoms compared to biological sex alone.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".