Creating a climate for therapist improvement: A case study of an agency focused on outcomes and deliberate practice.
Bibliographic record
Abstract
Recent evidence suggests that psychotherapists may not increase in effectiveness over accrued experience in naturalistic settings, even settings that provide access to patients' outcomes. The current study examined changes in psychotherapists' effectiveness within an agency making a concerted effort to improve outcomes through the use of routine outcome monitoring coupled with ongoing consultation and the planful application of feedback including the use of deliberate practice. Data were available for 7 years of implementation from 5,128 patients seen by 153 psychotherapists. Results indicate that outcomes indeed improved across time within the agency, with increases of d = 0.035 (p = .003) per year. In contrast with previous reports, psychotherapists in the current sample showed improvements within their own caseloads across time (d = 0.034, p = .042). It did not appear that the observed agency-level improvement was due to the agency simply hiring higher-performing psychotherapists or losing lower-performing psychotherapists. Implications of these findings are discussed in relation to routine outcome monitoring, expertise in psychotherapy, and quality improvement within mental health care. (PsycINFO Database Record
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".