Gender role identity, personality factors, and psychiatric symptoms among American adults: the Nathan Kline Institute Rockland Sample
Bibliographic record
Abstract
Introduction: Gender roles and personality traits have been reported to impact mental health. This study aims to investigate the relationship between gender role identity and psychiatric symptoms (anxiety, depressive symptoms, suicidality) as well as the moderating effects of personality traits in a community-representative sample of American adults. Methods: Data from 741 participants (65.7% females) were analyzed from the Nathan-Kline Institute - Rockland Sample database, a community-ascertained lifespan cohort with participants undergoing multimodal brain imaging and comprehensive behavioral, cognitive, and psychiatric assessments. This analysis is restricted to adults and uses well-validated questionnaires to assess gender role identity, personality traits, symptoms of anxiety and depression, and suicidal thoughts/behaviors. Results: Results revealed that having a gender role identity reversed to one's birth-assigned sex (i.e., feminine gender role in males and masculine gender role in females) was associated with poorer mental health (i.e., more anxiety and depressive symptoms). This effect was stronger in males where femininity was positively associated with more suicidal thoughts and behaviors. Further analyses revealed that only low-extroverted feminine males reported higher anxiety, and only high-neurotic feminine males reported higher suicidality. Conclusions: The present American study provides new understanding on gender role identity associations with mental health, while highlighting the importance of considering both birth-assigned sex and personality traits when studying gender role effects on psychiatric symptoms. We discuss the role of gendered traits and societal burden in relation to mental health.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".