A systematic review on the reliability and validity of semistructured diagnostic interviews for borderline personality disorder.
Bibliographic record
Abstract
There are many semistructured diagnostic interviews to assess borderline personality disorder (BPD), although the reliability and validity of these instruments has not been comprehensively reviewed. In this systematic literature review, Medline and PsycINFO databases were searched for studies evaluating the reliability and validity of diagnostic interviews used to assess BPD. Following a screening of search results and a review by independent raters, 53 studies were included in the review. Instruments were evaluated based on their reliability (interrater, test–retest, and internal consistency) and validity (convergent, discriminant, and criterion-related). Whereas many interviews have been extensively studied and received substantial support for their reliability and certain aspects of validity, some instruments were infrequently investigated and comparatively less data were available on their reliability and validity. Regardless of the diagnostic interview, dimensional assessments were consistently more reliable than categorical diagnoses of BPD. Overall, there was reasonably strong support for the reliability of semistructured diagnostic interviews for BPD, although continued work is needed to establish the validity of these instruments.
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 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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".