Validation of the French version of the Revised Diagnostic Interview for Borderlines (DIB-R) for assessing the psychopathology of borderline personality disorder
Bibliographic record
Abstract
BACKGROUND: Borderline personality disorder (BPD) is frequently subject to misdiagnosis or underdiagnosis. As a matter of fact, its evaluation poses several challenges, highlighting the importance of having validated evaluation instruments. The Revised Diagnostic Interview for Borderlines (DIB-R) is widely used and recognized for its validity when it comes to assessing the psychopathology of BPD, but, as for now, no French version of the interview exists. The aim of the current work is to validate a French version of the DIB-R. METHODS: The sample consists of N = 65 patients with borderline personality disorder (BPD) and N = 57 treatment seeking patients (non-BPD comparison group). For inter-rater reliability, a subsample of N = 84 interviews will be assessed by two raters, n = 47 for the BPD group and n = 37 for the non-BPD comparison group. RESULTS: To assess reliability, we conducted analyses of internal consistency and inter-rater reliability. The results were good for the overall interview as well as for the four domains of the DIB-R. To assess validity, we calculated the receiver operating characteristic (ROC) curve, sensitivity, specificity, predictive values, convergent and discriminative validity. The optimal cutoff was found to be 7. Regarding convergent validity, we found strong convergence between the Borderline Symptom List (BSL-23) and the DIB-R total score. Additionally, the two groups statistically differed on all the DIB-R scores, which indicates that the interview discriminates between the two groups. CONCLUSIONS: Our results indicate good psychometric properties of the French version of the DIB-R. This has important implications as the interview is useful both in clinical settings and for research purposes. Additionally, the present paper aims to contribute to the more general effort of demonstrating generalizability and transportability of the scale.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".