Strategies for Reducing Patient-Initiated Premature Termination of Psychotherapy
Bibliographic record
Abstract
Rates of patient-initiated premature termination in different forms of psychotherapy are consistently high. Patient-initiated premature termination is recognized as a significant obstacle to the effective and efficient use of psychotherapy. The literature describes many strategies for preventing premature termination, but lacks integration. This review attempts to provide a concise and comprehensive summary of the strategies that research or clinical experience have suggested may be useful for minimizing patient-initiated premature termination. A search was conducted on the MEDLINE, PsycINFO, and EMBASE databases for literature published between January 1970 and March 2004. Retrieved articles were published in English in peer-reviewed journals and focused on psychotherapy for adults. Thirty-nine publications that discussed strategies for preventing or reducing patient-initiated premature termination of psychotherapy were identified. Surprisingly, only 15 of these were research studies. Most of the retrieved literature consisted of clinical descriptions. The strategies can be assigned to nine categories: pretherapy preparation, patient selection, time-limited or short-term contracts, treatment negotiation, case management, appointment reminders, motivation enhancement, facilitation of a therapeutic alliance, and facilitation of affect expression. Research supports some of the strategies for reducing premature termination. However, methodologically sound studies of prevention strategies remain few in number.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".