Optimizing borderline personality disorder treatment by incorporating significant others: A review and synthesis.
Bibliographic record
Abstract
Borderline personality disorder (BPD) is a debilitating mental health condition that is highly associated with distress in close relationships and in romantic and nonromantic (e.g., familial) significant others. Interventions that efficiently improve BPD treatment outcomes are needed. Theory and research suggest that BPD may both promote and be maintained by close relationship distress. Incorporating significant others into BPD treatments may therefore present a novel and unique opportunity to maximize treatment outcomes. This work systematically reviews the empirical support for interventions that incorporate significant others in BPD treatment in addressing three potential treatment targets: (a) BPD pathology, (b) significant other distress, and (c) close relationship distress. Three distinct categories of intervention that involve significant others into BPD treatment are presented, and the interventions that fall within them are reviewed: (a) significant-other-assisted interventions, (b) education- and family-facilitated engagement interventions, and (c) disorder-specific interventions. Twelve articles outlining six treatments that vary in terms of potential treatment target, form of intervention, and level of empirical support are discussed. Interventions that target BPD in the context of close relationships, which are disorder-specific interventions, have amassed the most robust evidence base as an efficacious approach for all three targets at once. We conclude our review with a synthesis of the extant literature and offer future directions in terms of advancing theory to better understand and treat BPD. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".