Discrepancies in self- and informant-reports of personality pathology: Examining the DSM–5 Section III trait model.
Bibliographic record
Abstract
A proposed feature of personality pathology involves disturbances in identity, of which a lack of insight is one such manifestation. From recommendations in the literature, one potential approach to assess and quantify such impairment and link it to personality pathology, would be to obtain self-reports and informant reports and subsequently index the degree personality pathology severity exacerbates self-other discrepancies. The current study examines the degree to which self-reports and informant reports of Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), Section III trait scores are discrepant (i.e., mean-level discrepancies and correlational accuracy), as well as whether general personality pathology severity moderates these characteristics. Target participants (N = 208) in an elevated-risk community sample completed the Personality Inventory for DSM-5 (PID-5), and knowledgeable informants rated targets using the informant version of the PID-5. General personality pathology severity was assessed via an aggregate of five-factor model personality disorder prototype scores derived from self-report, informant-report, and interview ratings. Mean-level discrepancies and correlational accuracy (and their moderation by general personality pathology) for PID-5 domains, facets, and personality disorder scores were subsequently examined. Results suggested that targets tended to mostly rate themselves only slightly lower than informants across all PID-5 scores (median dz = .21), and correlational accuracy across all PID-5 scores was moderate (median r = .34). Importantly, however, mean-level discrepancies increased as general personality pathology severity scores increased. Implications and future directions for the multimethod assessment of dimensional personality pathology are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.013 | 0.038 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".