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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".