Is there room for criticism of studies of psychodynamic psychotherapy?
Bibliographic record
Abstract
Comments on the original article, "The efficacy of psychodynamic psychotherapy," by J. Shedler (see record 2010-02208-012). Shedler declared unequivocally that "empirical evidence supports the efficacy of psychodynamic therapy" (p. 98). He did not mention any specific criticisms that have been made of evidence on psychodynamic psychotherapies or address possible distinctions between evidence for short-term versus long-term psychodynamic psychotherapies. Instead, he attributed dissenting views to biases in evidence dissemination and review, which he suggested are rooted in a "lingering distaste in the mental health professions professions for past psychoanalytic arrogance and authority" related to a "hierarchical medical establishment that denied training to non-MDs and adopted a dismissive stance toward research" (Shedler, 2010, p. 98). Shedler (2010) justified his blanket dismissal of criticisms of evidence supporting psychodynamic psychotherapy on the basis of several published meta-analyses. The validity of conclusions from metaanalyses depends on the quality of the evidence synthesized, the nature of the studies included, and the rigor of the statistical analyses employed. Many meta-analyses, however, are not performed rigorously, which can result in treatment efficacy estimates that obscure important intertrial differences and that are unlikely to be replicated in clinical practice.
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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 | 0.042 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.059 | 0.062 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".