Where Sex Meets Gender: How Sex and Gender Come Together to Cause Sex Differences in Mental Illness
Bibliographic record
Abstract
Sex differences are prevalent in multiple mental disorders. Internalizing disorders are more commonly diagnosed in women, whereas externalizing and neurodevelopmental disorders are more often diagnosed in men. Significant sex/gender differences are reported in prevalence, symptom profile, age of onset, comorbidities, functional impairment, prognosis, as well as in responses to various treatments. In this conceptual article, we discuss theories and empirical studies of sex- and gender-related influences in mental health, by focusing on three examples: autism spectrum disorder (ASD), acknowledged as a disorder whose roots are mainly biological; eating disorders, whose origins are considered to be mainly psychosocial, and posttraumatic stress disorder (PTSD), an environmentally caused disorder with both psychosocial and biological underpinnings. We examine the ways in which sex differences emerge, from conception through adulthood. We also examine how gender dichotomies in exposures, expectations, role assumptions, and cultural traditions impact the expression of our three selected mental illnesses. We are especially interested in how sex-based influences and gender-based influences interact with one another to affect mental illness. We suggest that sex and gender are multi-faceted and complex phenomena that result in variations, not only between men and women, but also within each sex and gender through alterations in genes, hormone levels, self-perceptions, trauma experiences, and interpersonal relationships. Finally, we propose a conceptual diatheses-stress model, depicting how sex and gender come together to result in multiple sex/gender differences across mental disorders. In our model, we categorize diatheses into several categories: biological, intrapersonal, interpersonal, and environmental. These diatheses interact with exposure to stressors, ranging from relatively minor to traumatic, which allows for the sometimes bidirectional influences of acute and long-term stress responses. Sex and gender are discussed at every level of the model, thereby providing a framework for understanding and predicting sex/gender differences in expression, prevalence and treatment response of mental disorders. We encourage more research into this important field of study.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".