Intraclass Correlation Associated with Therapists: Estimates and Applications in Planning Psychotherapy Research
Bibliographic record
Abstract
It is essential that outcome research permit clear conclusions to be drawn about the efficacy of interventions. The common practice of nesting therapists within conditions can pose important methodological challenges that affect interpretation, particularly if the study is not powered to account for the nested design. An obstacle to the optimal design of these studies is the lack of data about the intraclass correlation coefficient (ICC), which measures the statistical dependencies introduced by nesting. To begin the development of a public database of ICC estimates, the authors investigated ICCs for a variety outcomes reported in 20 psychotherapy outcome studies. The magnitude of the 495 ICC estimates varied widely across measures and studies. The authors provide recommendations regarding how to select and aggregate ICC estimates for power calculations and show how researchers can use ICC estimates to choose the number of patients and therapists that will optimize power. Attention to these recommendations will strengthen the validity of inferences drawn from psychotherapy studies that nest therapists within conditions.
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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 | 0.222 | 0.578 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".