Is Longer-Term Psychodynamic Psychotherapy More Effective than Shorter-Term Therapies? Review and Critique of the Evidence
Bibliographic record
Abstract
BACKGROUND: In 2008, Leichsenring and Rabung performed a meta-analysis of 8 studies of longer-term psychodynamic psychotherapy (LTPP). The work was published in the Journal of the American Medical Association (vol. 300, pp 1551-1565), and they concluded that LTPP was more effective than shorter-term therapies. METHOD: Given that such claims have the potential to influence treatment decisions and policies, we re-examined the meta-analysis and the 8 studies. RESULTS: We found a miscalculation of the effect sizes used to make key comparisons. Claims for the effectiveness of LTPP depended on a set of small, underpowered studies that were highly heterogeneous in terms of patients treated, interventions, comparison-control groups, and outcomes. LTPP was compared to 12 types of comparison-controls, including control groups that did not involve any psychotherapy, short-term psychodynamic psychotherapy, and unvalidated treatments. Additionally, the studies failed to protect against threats to bias, and had poor internal validity. CONCLUSION: Overall, we found no evidence to support claims of superiority of LTPP over shorter-term methods of psychotherapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".