Tele-Mindfulness for Dementia's Family Caregivers: A Randomized Trialwith a Usual Care Control Group
Bibliographic record
Abstract
BACKGROUND: Caring for a family member with dementia is stressful and challenging. Family caregivers, as a vulnerable marginalized population and invisible backbone of the health care system, need accessible and effective interventions that are tailored to their particular needs. OBJECTIVES: The objective of this study was to evaluate the feasibility and effectiveness of a live online mindfulness-based cognitive therapy (tele-MBCT) intervention for family caregivers of individuals with dementia. METHODS: Family caregivers were assigned to a tele-MBCT intervention or a usual care control group. Tele-MBCT participants attended eight weekly live online training and practiced mindfulness practices at home. All participants completed surveys at baseline, post-intervention, and 4-week follow-up. RESULTS: 26 participants (age 60±13 years) were enrolled and randomized (14 in the intervention and 12 in the control group), and 92.3% completed the study. 88% of the participants were female, and 70% were caring for a parent for a mean of 5.12±2.88 years. 84% of the participants in the intervention group attended at least seven sessions and the average of daily practice was 23.58±45.71 minutes. All participants were satisfied with the intervention, and 88.8% were satisfied with the online delivery method. Participants in the intervention group showed Pre-Post improvement in self-compassion (t (11) = -2.49, p=0.03) and coping strategies (t (11) = 3.62, p=0.004) compared to the control group. CONCLUSION: Tele-MBCT is a feasible intervention and may improve psychological outcomes and adaptive coping in family caregivers of individuals with dementia. A larger controlled trial is warranted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.011 | 0.001 |
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".