Interventions for Caregivers of Older Adults with Dementia Living in the Community: A Rapid Review of Reviews
Bibliographic record
Abstract
This rapid review of systematic reviews examines non-professional interventions that have been implemented to support family caregivers of older adults with dementia who are living in the community. There is a robust body of empirical literature examining such interventions for family caregivers; therefore, this rapid review includes only systematic reviews. MEDLINE, CINAHL, and EMBASE databases were searched from September 2020 to December 2020, and 19 systematic reviews were selected for a full review. Psychosocial, psychoeducational, social support, and multicomponent interventions consistently show positive impacts on a variety of outcomes. The evidence suggests that multicomponent interventions that are tailored to the needs of individual caregivers are the most impactful interventions and should be utilized in future program development. The most effective combination of interventions is unknown and warrants further investigation. However, the repeated success of psychoeducational, psychosocial, and social support interventions suggests that when used together, they may be a successful combination that contributes to positive impacts on caregivers. This multicomponent intervention should be flexible, as interventions are most effective when they are tailored to the individual needs of caregivers and adapted over time as the needs of the caregiver and person living with dementia change with disease progression.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".