Childhood maltreatment and personality disorders in patients with a major depressive disorder: A comparative study between France and Togo
Bibliographic record
Abstract
Few studies have examined the association between childhood maltreatment (CM) and personality disorders (PDs) in adulthood in two different cultural contexts, including sub-Saharan Africa. The aims of this study were to compare the frequency of CM between patients in treatment in France and Togo for a major depressive disorder (MDD), to explore the link between CM and PDs, and to examine the mediating effect of personality dimensions in the pathway from CM to PDs in 150 participants (75 in each country). The 28-item Childhood Trauma Questionnaire, the International Personality Item Pool, and the Personality Diagnostic Questionnaire (PDQ-4+) were used to assess CM, personality dimensions, and PDs respectively. Togolese participants reported sexual and physical abuse (PA) and emotional and physical neglect significantly more frequently than French participants. In Togo, severe PA was associated with schizoid, antisocial, narcissistic, obsessive-compulsive, depressive, and negativist PDs whereas in France, PA was only linked to paranoid PD. In Togo, emotional instability partly mediated the relationship between CM and PDs while in France, no personality dimension appeared to mediate this link. Our results support the hypothesis that CM is more common in low-income countries and suggest that the links between CM and PDs are influenced by social environment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".