Empirically supported psychodynamic psychotherapy for common mental disorders–An update applying revised criteria: Systematic review protocol
Bibliographic record
Abstract
The approach of evidence-based medicine has been extended to psychotherapy. More than 20 years ago, criteria for empirically supported psychotherapeutic treatments (ESTs) were defined. Meanwhile a new model for empirically supported psychotherapeutic treatments has been proposed. While the empirical status of psychodynamic therapy (PDT) was assessed in several reviews using the previous criteria, the proposed new model has not yet been applied to PDT. For this reason, we will carry out a systematic review on studies of PDT in common mental disorders applying the revised criteria of ESTs. As suggested by the new model we will focus on recent systematic quantitative reviews. A systematic search for meta-analyses on the efficacy of PDT in common mental disorders will be carried out. Meta-analyses will be selected and evaluated by at least two raters along the criteria of the new proposed model. In addition, systematic reviews and individual studies addressing mechanisms of change in PDT, effectiveness under real-world conditions, cost-effectiveness and adverse events will be systematically searched for and evaluated. Finally, quality of evidence, the extent to which benefits exceed harms and strength of recommendations will be assessed per disorder using GRADE.
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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 | 0.068 | 0.078 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.039 | 0.006 |
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".