Sex and Gender Roles in Relation to Mental Health and Allostatic Load
Bibliographic record
Abstract
OBJECTIVES: Beyond male/female binaries, gender roles represent masculine and feminine traits that we assimilate and enact throughout life span development. Bem proposed that "androgynous" individuals adeptly adapt to different contexts by alternating from a strong repertoire of both masculine and feminine gender roles. By contrast, "undifferentiated" individuals may not adapt as well to social norms because of weak self-endorsed masculinity and femininity. METHODS: Among 204 adults (mean [standard error] age = 40.4 [0.9] years; 70% women) working in a psychiatric hospital, we hypothesized that androgynous individuals would present better mental health and less physiological dysregulations known as allostatic load (AL) than undifferentiated individuals. AL was indexed using 20 biomarkers using the conventional "all-inclusive" formulation that ascribes cutoffs without regard for sex or an alternative "sex-specific" formulation with cutoffs tailored for each sex separately while controlling for sex hormones (testosterone, estradiol, progesterone). Well-validated questionnaires were used. RESULTS: Independent of sex, androgynous individuals experienced higher self-esteem and well-being and lower depressive symptoms than did undifferentiated individuals. Men manifested higher AL than did women using the all-inclusive AL index (p = .044, ηP = 0.025). By contrast, the sex-specific AL algorithm unmasked a sex by gender roles interaction for AL (p = .043, ηP = 0.048): with the highest AL levels in undifferentiated men. Analysis using a gender index based on seven gendered constructs revealed that a greater propensity toward feminine characteristics correlated only with elevated sex-specific AL (r = 0.163, p = .025). CONCLUSIONS: Beyond providing psychobiological evidence for Bem's theory, this study highlights how sex-specific AL formulations detect the effects of sociocultural gender.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".