Personality Disorders and the Five-Factor Model among French Speakers in Africa and Europe
Bibliographic record
Abstract
Objective: To describe the relation between personality disorders (PDs) and the Five-Factor Model (FFM)—a dimensional model describing normal personality traits known for its invariance across cultures—in 2 different cultural settings. Several authors have suggested that PDs may be more accurately described using a dimensional model instead of a categorical one. Method: Subjects from 9 French-speaking African countries ( n = 2014) and from Switzerland ( n = 697) completed both the French version of the International Personality Disorder Examination screening questionnaire, assessing the 10 DSM-IV PDs, and the French version of the Revised NEO Personality Inventory, assessing the 5 domains and 30 facets of the FFM. Results: Correlations between PDs and the 5 domains of the FFM were similar in both samples. For example, neuroticism was highly correlated with borderline, avoidant, and dependent PDs in both Africa and Switzerland. The total rank-order correlation (rho) between the 2 correlation matrices was very high (rho = 0.93) and significant ( P < 0.001), as were the rhos for all domains of the FFM and all PDs, except paranoid and dependent PDs. However, the rhos for PDs across facet scales were all highly significant ( P < 0.001). Moreover, 80% of Widiger and colleagues' predictions and 70% of Lynam and Widiger's prototypes, concerning the relation between PDs and the FFM, were confirmed in both samples. Conclusions: The relation between PDs and the FFM was stable in 2 samples separated by a great cultural distance. These results suggest that a dimensional approach and in particular the FFM may be useful for describing PDs in various cultural settings. Objectif: Décrire la relation entre les troubles de la personnalité (TP) et le Modèle en cinq facteurs (MCF) — un modèle dimensionnel décrivant les traits de personnalité normaux réputé pour son invariance entre les cultures — dans 2 milieux culturels différents. Plusieurs auteurs ont suggéré que les TP peuvent être décrits plus précisément à l'aide d'un modèle dimensionnel plutôt que catégorique.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".