Personality Disorders among Sexual and Gender Minority Populations
Bibliographic record
Abstract
Abstract Research examining the prevalence, impact, and course of personality disorders in sexual and gender minority populations is sparse; however, the available literature suggests that personality pathology is more prevalent in sexual and gender minorities compared to those who identify as heterosexual and/or cisgender. Although research is limited, several competing hypotheses have attempted to explain this disparity, including environmental, developmental, minority stress, and dual marginalization theories, as well as critical theories that point to possible roles of diagnostic, clinician, and cultural biases. This chapter highlights three critical future directions. First, rigorous longitudinal research needs to be conducted to evaluate competing etiological hypotheses of personality disorders in sexual and gender minorities. Second, future personality research should examine through an intersectional lens how additional aspects of one’s identity (e.g., ethnicity, class) interact with sexual orientation and gender to influence the experiences of these groups. Finally, clinicians and researchers must be sensitive to both the need to accurately document personality pathology, and the need to avoid unnecessarily pathologizing the experiences of sexual and gender minorities. Ultimately, addressing these future directions would enhance clinicians’ and researchers’ understanding of and ability to respond to the mental health needs of sexual and gender minority populations.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".