Gender role strain, core schemas, and psychotic experiences in ethnically diverse women: A role for sex‐ and gender‐based analysis in psychosis research?
Bibliographic record
Abstract
Negative -self and -others core schemas have been implicated in the development and maintenance of psychotic experiences. One component of the self-system is gender-role strain (GRS; perceived discrepancy between actual self and gender-role norms). Although the role of gender in the formation of core schemas has been underscored in social and developmental psychology literatures, GRS has not been investigated in relation to psychosis. We examined whether it might be associated with negative schemas and psychotic experiences in women consistent with the trend toward sex- and gender-based analysis (SGBA) in health research. Forty-four women with a schizophrenia spectrum disorder diagnosis and 48 female nonclinical participants completed a series of questionnaires measuring GRS, femininity ideology, core schemas, childhood trauma, and implicit femininity stereotypes (The Gender Stereotype Implicit Association Test). Half the total sample comprised women with minority-ethnic status. Women in the psychosis group reported higher levels of GRS than comparison participants. Differences in endorsement of femininity ideology between the two groups narrowly missed significance with a trend toward greater femininity ideology in the psychosis group and significantly greater endorsement of the sexual purity domain for minority-ethnic women. There was no difference in implicit femininity stereotypes. Analyses suggested that the relationship between GRS and symptoms was mediated by negative -self and -others schemas. Childhood sexual trauma, though higher for women with psychosis, was associated with gender-role strain in the nonclinical sample only. Findings warrant further investigation with larger samples. SGBA has the potential to fill gaps in our current knowledge with regard to psychosis theory, research, and practice.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".