Examining the feasibility and effectiveness of case manager delivered problem-solving therapy on late-life depression in a real-world setting: a mixed design pilot study
Why this work is in the frame
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Bibliographic record
Abstract
To optimize outcomes in Late-Life Depression (LLD), there remains a need for innovative augmentation treatments. Problem-Solving Therapy (PST) is demonstrated as an effective augmentation psychotherapy for LLD in a one-to-one setting. This study investigated the feasibility of implementing Case Manager delivered group PST for LLD in a community setting. The design was a mixed-methods pilot one-group pre-test post-test quasi-experimental feasibility study in adults aged 60+ years meeting criteria for major depressive disorder (MDD). Feasibility measures were inclusion, attendance, and attrition rate. PST was delivered in groups of 6–9 participants over 8 weeks. Participants completed a weekly self-rated depression scale from weeks 1–8, and pre-and post-intervention rater assessed depression and self-rated assessments of quality of life, insomnia, disability, and anxiety. Focus group interviews took place at week 8 for the first and last study cohort. Twenty-nine participants (inclusion rate of 91%) were enrolled, a recruitment rate of 2.2 participants/month. Of these, 26 (90%) completed week 8 assessments and 25 (96%) attended 5 or more PST sessions, while 15 (58%) attended all sessions. Self-rated depression, anxiety, and insomnia scores decreased significantly with medium-to-large effect sizes, while there was non-significant improvement in rater-assessed depression and self-rated disability and quality of life. Qualitative data showed participants found PST an easy-to-use technique and felt empowered and hopeful in recovering from depression. The results justify a replication of the study in a large RCT exploring the efficacy of offering a group-based Case Manager facilitated PST to older adults with LLD, in a community setting. Clinical Trial Registration Number: NCT03408821
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it