Dropout in Psychotherapy for Personality Disorders: A Systematic Review of Predictors
Bibliographic record
Abstract
INTRODUCTION: Dropout in psychotherapy for personality disorders is a major challenge, affecting treatment efficacy and mental health care delivery. Influenced by patient characteristics, therapist factors and treatment dynamics, dropout remains prevalent. This systematic review identifies predictors of psychotherapy dropout in individuals with personality disorders to inform strategies that enhance treatment engagement. METHOD: A systematic search in PsycINFO, PubMed and Scopus identified 22 studies from 1976 articles. Inclusion criteria required DSM/ICD-based personality disorder assessments and dropout predictors in psychotherapy. Non-English or non-peer-reviewed studies were excluded. Screening followed PRISMA guidelines using Rayyan, and study quality was assessed with the Newcastle-Ottawa Scale (NOS). RESULTS: Dropout rates ranged from 10.4% to 58%, depending on treatment modality and patient characteristics. Younger age, comorbid substance use disorders, emotional dysregulation, distress tolerance difficulties, childhood emotional abuse, therapist turnover and low motivation were significant predictors of dropout. Conversely, strong therapeutic alliances, mindfulness-based skills and engagement in phone coaching were associated with improved retention. Other relevant factors included low reflective functioning, lower education levels and socio-economic adversity, such as receiving disability benefits. Only one study identified low reflective functioning as a dropout predictor. Systemic factors, including treatment organization and care coordination, also played a crucial role. CONCLUSIONS: Addressing dropout requires early engagement strategies, therapist continuity and treatment flexibility. Enhancing therapeutic alliance and reflective functioning may be particularly effective in reducing dropout. Systemic improvements, such as better care coordination and accessibility, are crucial for sustaining engagement and improving psychotherapy outcomes for individuals with personality disorders. REGISTRATION: PROSPERO number: CRD42024509283.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".