Good Psychiatric Management of Borderline Personality Disorder: Foundations and Future Challenges
Bibliographic record
Abstract
Borderline personality disorder is a common condition characterized by numerous comorbid conditions, frequent use of clinical services, and an elevated lifetime risk for suicide. Good psychiatric management (GPM) was developed for patients with borderline personality disorder with the purpose of supporting wider community adoption and dissemination compared with existing therapies. The authors aimed to review the foundations and development of GPM, in particular the initial Canadian study assessing the therapy. They then reviewed the progress in research arising from the initial study and explored the research and educational opportunities needed to further the development of GPM for patients with borderline personality disorder. Research has indicated that patients with borderline personality disorder with complex comorbid conditions and impulsivity may benefit from GPM. Future research needs include noninferiority and equivalence studies comparing GPM with another evidence-based treatment; studies demonstrating that evidence-based therapies for borderline personality disorder improve functioning; and research on more accessible therapies, mechanisms of action for evidence-based therapies, extending therapies to patients with borderline personality disorder and significant comorbid conditions, and modifying therapies for men with borderline personality disorder. Attention should be directed toward testing stepped care models and integrating therapies such as GPM into psychiatric training programs. GPM is in development but shows promise as a therapy that is effective and accessible and that can be widely disseminated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".