Psychologists' Perspectives on Therapy Termination and the Use of Therapy Engagement/Retention Strategies
Bibliographic record
Abstract
Practicing psychologists (n = 269) were surveyed regarding their perspectives on client reasons for termination at different points in therapy and their use of strategies to engage and retain clients in therapy. Psychologists estimated that one-third of their caseload unilaterally terminated (M = 13% before the third therapy session; M = 20% after the third session). They viewed lack of readiness for change/insufficient motivation as the most important barrier to early treatment engagement, and symptom improvement as the most important reason for clients' unilateral decisions to end therapy after the third session. Most psychologists reported occasional use of the majority of engagement and retention strategies. Although some strategies were used by most psychologists (e.g., building the early working alliance), fewer than 25% of psychologists reported the frequent use of time-limited treatment, appointment reminders or case management procedures. As the implementation of these strategies in clinical practice has the potential to greatly influence client retention rates, future research should examine psychologists' perspectives on and barriers to using these strategies. Copyright © 2016 John Wiley & Sons, Ltd. KEY PRACTITIONER MESSAGE: Therapists tend to underestimate the number of clients who make unilateral decisions to end treatment in their own practices. Therapists are unlikely to take steps to engage and retain clients in treatment unless they believe that unilateral termination is a significant problem. Clients who unilaterally end treatment are often experiencing problems with the process of therapy (e.g., dissatisfaction, lack of fit, feeling as though therapy is going nowhere), whereas therapists often attribute failed therapy to clients. It is important to be aware of this tendency and look for other explanations. It is worthwhile to actively solicit clients' barriers in an effort to mitigate them. The empirical literature provides ample evidence that it is helpful for therapists to deliberately employ strategies to engage and retain clients in therapy. All therapists would benefit from considering which strategies fit with their practices. Although almost all therapists emphasize building the early working alliance, and this is essential to good outcome, other evidence-based methods of engaging clients in therapy are largely underutilized, such as systematically monitoring client progress and barriers, placing time limits on treatment, using appointment reminders, and case management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".