New developments in psychosocial interventions for adults with unipolar depression
Bibliographic record
Abstract
PURPOSE OF REVIEW: Depression treatment guidelines typically recommend cognitive behavioral therapy and/or interpersonal therapy for the acute treatment of mild-moderate depression. However, several new developments support an expanded role for psychotherapy in depression treatment. This article summarizes recent psychotherapy efficacy studies across the depression treatment continuum and the effectiveness of psychosocial interventions in community settings. RECENT FINDINGS: New psychotherapies in the acute treatment of mild-moderate depression include emotion-focused therapy, self-system therapy, cognitive control training and positive psychotherapy. Furthermore, emerging evidence supports the use of psychotherapy for moderate-severe and treatment-resistant depression and for recurrent depression with a seasonal pattern. An important area of growth is the development and evaluation of continuation/maintenance treatments based on cognitive behavioral therapy and interpersonal therapy to reduce depressive relapse risk in recurrent and chronic depression. Finally, there is evidence supporting the effectiveness of stepped care, chronic disease management and collaborative care models in community settings. SUMMARY: Emerging evidence supports an expanded role for the use of psychosocial interventions as acute and continuation/maintenance treatments for unipolar depression. Although further research is required to replicate these findings, a remaining challenge is to increase the availability of these treatments to the mental health consumer.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".