Patient‐related factors associated with patient retention and non‐completion in psychosocial treatment of borderline personality disorder: A systematic review
Bibliographic record
Abstract
The potential efficacy of psychosocial interventions in the treatment of borderline personality disorder (BPD) is impacted by significant treatment non-completion (TNC), with meta-analytic studies reporting rates of attrition of between 25% and 28%. Increasing patient retention could facilitate outcomes and improve resource utilization, given limited healthcare services. A systematic search of PsycINFO, CINAHL, EMBASE, CENTRAL, and Web of Science Core Collection identified 33 articles that met the criteria for inclusion. Although substantial heterogeneity in terms of methodology and quality of analysis limited conclusions that could be drawn in the narrative review, a few consistent patterns of findings were elucidated, such as Cluster B personality disorder comorbidities and lower therapeutic alliance were associated with TNC. Interestingly, the severity of BPD symptoms was not a predictor of TNC. These findings are discussed in terms of their potential theoretical contribution to TNC. Clinically, there may be value in applying mindfulness and motivational interviewing strategies early on in treatment for individuals who present uncertainty about engaging in treatment. Further research to develop this empirical landscape includes focusing on high-powered replications, examining burgeoning lines of research, and investigating dynamic predictors of TNC.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".