How many therapists? Practical guidance on investigating therapist effects in randomized controlled trials for eating disorders
Bibliographic record
Abstract
OBJECTIVE: An important question in implementation/dissemination research is whether the efficacy of a given treatment varies in part based on the therapist delivering the treatment. This study sought to provide practical guidance to researchers in the field of eating disorders for building measurement of therapist effects into the design of a typical, relatively small randomized controlled trial (RCT). METHOD: Using assumptions based on past trials of eating disorder treatments, Monte Carlo simulations were used to examine 12 different scenarios based on crossing the number of therapists (between two and five) and the estimated therapist effect size (small, medium, and large). Patient sample size and study design were held constant. RESULTS: There was reasonable power (≥70%) to detect the therapist effect with three or four therapists and a large effect size. DISCUSSION: Several practical implications for testing therapist effects in RCT 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 | 0.506 | 0.765 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.027 | 0.018 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".