Control condition design and implementation features in controlled trials: a meta-analysis of trials evaluating psychotherapy for depression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT: Control conditions are the primary methodology used to reduce threats to internal validity in randomized controlled trials (RCTs). This meta-analysis examined the effects of control arm design and implementation on outcomes in RCTs examining psychological treatments for depression. A search of MEDLINE, PsycINFO, and EMBASE identified all RCTs evaluating psychological treatments for depression published through June 2009. Data were analyzed using mixed-effects models. One hundred twenty-five trials were identified yielding 188 comparisons. Outcomes varied significantly depending control condition design (p < 0.0001). Significantly smaller effect sizes were seen when control arms used manualization (p = 0.006), therapist training (p = 0.002), therapist supervision (p = 0.009), and treatment fidelity monitoring (p = 0.003). There were no significant effects for differences in therapist experience, level of expertise in the treatment delivered, or nesting vs. crossing therapists in treatment arms. These findings demonstrate the substantial effect that decisions regarding control arm definition and implementation can have on RCT outcomes.
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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 | 0.544 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.077 | 0.021 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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 it