Why is an evidence‐based classification of personality disorder so elusive?
Bibliographic record
Abstract
Despite recent revisions, the classification of personality disorder remains a matter of dispute, and there is little evidence of consistent progress toward an evidence-based system. This essay examines four issues impeding taxonomic progress and explores how they might be addressed. First, the phenomenological and aetiological complexity of personality disorder poses a formidable challenge to traditional taxonomic methods. Second, current classifications incorporate assumptions such as a stringent version of medical model and an essentialist philosophy that are inconsistent with empirical evidence. Third, despite the claims of trait psychology, a viable alternative to categorical diagnosis is not available. Contemporary trait models have not gained widespread clinical acceptance and substantial conceptual and methodological limitations compromise their clinical value. Finally, the processes used to revise official classifications are biased toward conservative revisions and difficult to shield from non-scientific influences. It is suggested that rather making further attempts to develop a general monolithic classification that meets all needs, consideration be given to developing a more flexible and multifaceted framework that combines diagnosis and assessment. © 2020 John Wiley & Sons, Ltd.
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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 | 0.129 | 0.292 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.008 | 0.023 |
| 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".