The efficacy of mindfulness‐based cognitive therapy to improve depression symptoms and quality of life in individuals with memory difficulties and caregivers: A short report
Bibliographic record
Abstract
Abstract Introduction Depression symptoms are common for older adults with memory difficulties and their caregivers. Mindfulness‐based cognitive therapy (MBCT) reduces the risk of relapse in recurrent depression and improves depression symptoms. We explored recruitment and retention success and preliminary effect sizes of MBCT on depression and anxiety symptoms, as well as mindfulness facets, in individuals with memory difficulties and their caregivers. Methods A difficulty with memory group (DG) and caregiver group (CG) were randomized into either the MBCT intervention or waitlist control. After serving as controls, participants received the intervention. Mean pre–post changes by group were compared and effect sizes computed. Correlations between mindfulness facets and depression symptoms are also presented. Results Only 47% of the initial participants completed the study. The intervention did not have an effect on the outcome variables examined. However, improvements in non‐judgmental scores were associated with reductions in the number of depression symptoms reported by DG participants (r = –0.90, 95% confidence interval [CI]: –0.98, –0.52) and CG participants (r = –0.76, 95% CI: –0.95, –0.19). Furthermore, improvements in awareness scores (r = –0.69, 95% CI: –0.93, –0.05) and level of burden (r = 0.87, 95% CI: 0.49, 0.97) also significantly correlated with reduced depression symptoms in the CG group. Conclusions By determining preliminary MBCT effect sizes in individuals with memory difficulties and their caregivers, research with larger, controlled samples is now justified to determine the true effects of MBCT in these populations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".