Reducing Burden for Caregivers of Older Adults With Mild Cognitive Impairment: A Systematic Review
Bibliographic record
Abstract
BACKGROUND/RATIONALE: This systematic review aims to evaluate the efficacy of the nonpharmacological interventions reducing burden, psychological symptoms, and improving quality of life of caregivers of individuals with mild cognitive impairment (MCI). METHOD: Databases reviewed included Medline, Cochrane Library, Embase, PsycNet, AgeLine, and ProQuest Dissertations and Theses. Studies using an experimental/quasi-experimental design including nonpharmacological intervention were included. Four studies were included, and no meta-analysis was conducted. RESULTS: Calendar training and note-taking (cognitive intervention) significantly decreased caregiver's depressive symptoms and prevented worsening of subjective burden 6 months posttreatment. Daily engagement of meaningful activity combined with problem-solving therapy and educational material reduced depressive symptoms 3 months posttreatment. Moreover, educational intervention and social conversation phone calls decreased caregiver burden 3 months posttreatment. CONCLUSION: Studies suggest that nonpharmacological interventions can support caregivers of older adults with MCI, but the few published articles present some bias and are inconclusive. Randomized-controlled trials targeting specifically caregivers are needed to determine the most efficient type of interventions for those individuals.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".