Reconsidering the role of sex hormones in psychopathy development: Estrogen and psychopathy among male justice‐involved youth
Bibliographic record
Abstract
Prominent theory suggests that factor one psychopathic traits may develop from increased input from hormones in the hypothalamic pituitary gonadal axis (HPG; i.e., testosterone) and decreased input from the hypothalamic pituitary adrenal axis (HPA; i.e., cortisol). Although there are extensive findings connecting low cortisol to psychopathy, less support has emerged for high levels of testosterone. This study examined whether incorporating the HPG hormone, estradiol, into this model would reveal relationships in line with theory: high levels of estradiol and testosterone in combination with low levels of cortisol would inform psychopathic traits. Baseline and reactive hormone levels were measured and compared to Psychopathy Checklist-Youth Version (PCL-YV) interviews among 66 male justice-involved youth (M age = 15.73) in a Southeastern juvenile detention center. The primary findings of this study were relationships between interacting HPA and HPG axis hormones with facet one and facet two psychopathic traits. Specifically, psychopathy total scores, interpersonal traits, and affective traits related to estradiol and testosterone reactivity, in that psychopathy scores were more likely with decreases in hormone reactivity (i.e., change in hormone level) following a stressor. Moreover, affective traits related to reactivity in all three hormones. These findings support inclusion of estradiol in neurobiological models of psychopathy and consideration of the individual components of psychopathy. This study adds to the growing body of research supporting interactions between variations in functioning of the HPA and HPG axes in relation to psychopathy.
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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 | 0.001 | 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 | 0.001 | 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) | 0.001 | 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".