Supervisor variance in psychotherapy outcome in routine practice
Bibliographic record
Abstract
OBJECTIVE: Although supervision has long been considered as a means for helping trainees develop competencies in their clinical work, little empirical research has been conducted examining the influence of supervision on client treatment outcomes. Specifically, one might ask whether differences in supervisors can predict/explain whether clients will make a positive or negative change through psychotherapy. METHOD: In this naturalistic study, we used a large (6521 clients seen by 175 trainee therapists who were supervised by 23 supervisors) 5-year archival data-set of psychotherapy outcomes from a private nonprofit mental health center to test whether client treatment outcomes (as measured by the OQ-45.2) differed depending on who was providing the supervision. Hierarchical linear modeling was used with clients (Level 1) nested within therapists (Level 2) who were nested within supervisors (Level 3). RESULTS: In the main analysis, supervisors explained less than 1% of the variance in client psychotherapy outcomes. CONCLUSIONS: Possible reasons for the lack of variability between supervisors are discussed.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".