Meta‐analysis of problem solving therapy for the treatment of major depressive disorder in older adults
Bibliographic record
Abstract
OBJECTIVE: Major depressive disorder (MDD) affects many older adults and is associated with poor medical and mental health outcomes. Problem Solving Therapy (PST) has emerged as a promising psychotherapy for MDD in older adults, although the efficacy of PST in this population has not been well described. We examined the effectiveness of PST for the treatment of MDD in older adults in a systematic review and meta-analysis. METHODS: We searched electronic databases to identify randomized controlled trials comparing PST to a control condition or other treatment for MDD in adults with an average age of 60 years or older. We used meta-analysis to arrive at pooled summary measures of the efficacy of PST when compared to control conditions on the change in depressive symptoms and other outcomes. RESULTS: Nine studies with a total of 569 participants (290 PST, 279 control) met inclusion criteria. Most studies administered PST in person and were between 6 and 12 weeks in duration. Meta-analysis of six studies evaluating the effect of PST on depression using the Hamilton Rating Scale for Depression identified a significant reduction in depression associated with PST (pooled mean difference = -6.94, 95%CI -10.91 to -2.97, d = 1.15, P = 0.0006). PST was also effective in reducing disability in studies reporting this outcome. CONCLUSIONS: Our review supports the existing research literature on PST suggesting that it is an effective treatment for older people with MDD. Further study is required to understand long-term outcomes associated with PST and its efficacy when compared to other treatments.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.042 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".