Scoring the DSM-IV Personality Disorders Using the Five-Factor Model: Development and Validation of Normative Scores for North American, French, and Dutch-Flemish Samples
Bibliographic record
Abstract
Five-Factor Model (FFM) personality disorder (PD) counts have demonstrated significant convergent and discriminant validity with DSM-IV PD symptoms. However, these FFM PD counts are of limited clinical use without normative data because it is difficult to determine what a specific score means with regard to the relative level of elevation. The current study presents data from three large normative samples that can be used as norms for the FFM PD counts in the respective countries: United States (N = 1,000), France (N = 801), and Belgium-Netherlands (N = 549). The present study also examines the performance, with regard to diagnostic efficiency, of statistically-defined cut-offs at 1.5 standard deviations above the mean (T > or = 65) versus previously identified cut-offs using receiver-operator characteristics (ROC) analyses. These cut-offs are tested in three clinical samples-one from each of the aforementioned countries. In general, the T > or = 65 cut-offs performed similarly to those identified using ROC analyses and manifested properties relevant to a screening instrument. These normative data allow FFM data to be used in a flexible and comprehensive manner, which may include scoring this type of personality data in order to screen for DSM-IV PD constructs.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".