Evaluating the evidential value of empirically supported psychological treatments (ESTs): A meta-scientific review.
Bibliographic record
Abstract
Empirically supported treatments (or therapies; ESTs) are the gold standard in therapeutic interventions for psychopathology. Based on a set of methodological and statistical criteria, the APA has assigned particular treatment-diagnosis combinations EST status and has further rated their empirical support as Strong, Modest, and/or Controversial. Emerging concerns about the replicability of research findings in clinical psychology highlight the need to critically examine the evidential value of EST research. We therefore conducted a metascientific review of the EST literature, using clinical trials reported in an existing online APA database of ESTs, and a set of novel evidential value metrics (i.e., rates of misreported statistics, statistical power, R-Index, and Bayes Factors). Our analyses indicated that power and replicability estimates were concerningly low across almost all ESTs, and individually, some ESTs scored poorly across multiple metrics, with Strong ESTs failing to continuously outperform their Modest counterparts. Lastly, we found evidence of improvements over time in statistical power within the EST literature, but not for the strength of evidence of EST efficacy. We describe the implications of our findings for practicing psychotherapists and offer recommendations for improving the evidential value of EST research moving forward. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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How this classification was reachedexpand
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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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; both teacher heads 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".