A comparison of the associations of the Diagnostic and Statistical Manual of Mental Disorders, fifth edition, Section II personality disorders and Section III personality domains with clinical dysfunction in a psychiatric patient sample.
Bibliographic record
Abstract
= 185). To this end, a series of hierarchical regression analyses was conducted in which the 10 SII-PDs and the five AMPD trait domains served as the predictor variables and five areas of clinical dysfunction as the criterion variables. Two models for each criterion were tested. In Model A, the 10 PDs were entered as a block, followed by the block entry of trait domains; in Model B, the block entry of these predictors was reversed. As the AMPD was designed to address the shortcomings of the SII-PDs, it was hypothesized that the AMPD trait domains would show greater predictive capacity vis-à-vis the latter by (a) explaining more overall variance for each criterion variables when entered first into the model versus when SII-PDs was entered first and (b) explaining more incremental variance than SII-PDs when block was entered second. These hypotheses were partially supported. Overall, the AMPD trait domains predicted more variance than SII-PDs and demonstrated better model fit and more predictive power for three of the criterion variables. Similarly, the AMPD domains predicted a significant but modest incremental increase in variance over that of the SII-PDs for three of the criterion variables. We conclude that more work needs to be done to improve the AMPD, particularly in the assessment of externalizing psychopathology as it relates to clinical dysfunction. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".