Culture and personality disorder
Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim of this review is twofold: to review recent literature on personality disorders, published in 2013 and the first half of 2014; and to use recent theoretical work to argue for a contextually grounded approach to culture and personality disorder. RECENT FINDINGS: Recent large-sample studies suggest that U.S. ethnoracial groups differ in personality disorder diagnostic rates, but also that minority groups are less likely to receive treatment for personality disorder. Most of these studies do not test explanations for these differences. However, two studies demonstrate that socioeconomic status partly explains group differences between African-Americans and European Americans. Several new studies test the psychometric properties of instruments relevant to personality disorder research in various non-Western samples. Ongoing theoretical work advocates much more attention to cultural context. Recent investigations of hikikomori, a Japanese social isolation syndrome with similarities to some aspects of personality disorder, are used to demonstrate approaches to contextually grounded personality disorder research. SUMMARY: Studies of personality disorder must understand patients in sociocultural context considering the dynamic interactions between personality traits, developmental histories of adversity and current social context. Research examining these interactions can guide contextually grounded clinical work with patients with personality disorder.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".