Interventions for caregivers of older adults with dementia living in the community: A rapid review
Bibliographic record
Abstract
Abstract Background This rapid review examined the literature regarding interventions, including volunteer navigation, that have been implemented to address the needs of caregivers of older adults with dementia living in the community. It is essential to understand the efficacy of interventions in meeting the needs of family and friend caregivers to ensure that the development of new interventions is evidence‐based, and will contribute to positive impacts for caregivers. Method MEDLINE, CINHAL and EMBASE databases were searched. There is a robust body of empirical literature addressing the needs of caregivers, therefore, the rapid review focused on evidence from systematic reviews. Research question: “What is the empirical evidence on interventions (including navigation) to meet the needs of family caregivers of persons living with dementia?”. 19 systematic reviews were included for review. Result 1) Psychosocial, psychoeducational, social support and multicomponent interventions consistently had positive impacts on a variety of outcomes. 2) Multicomponent interventions that are tailored to the needs of individual caregivers are the most effective interventions and should be utilized in future program development. 3) The most effective combination of interventions that should be incorporated into multicomponent interventions is unknown and should be investigated further. Conclusion Psychoeducational, psychosocial, social support, and multicomponent interventions consistently result in positive impacts on a variety of outcomes. However, the most impactful combination of specific interventions that should be utilized in multicomponent interventions is unclear. Despite this uncertainty, the repeated success of psychoeducational, psychosocial and social support interventions suggests that these components should be utilized in conjunction with one another in multicomponent interventions that are tailored to the needs of individuals. In order to accomplish this, future programs need to provide adequate time for caregivers and intervention administrators to develop meaningful relationships in which the caregiver feels comfortable to share their individual needs, and the administrator can truly understand them. Nav‐CARE may serve as a program that can incorporate psychosocial, psychoeducational and social support aspects of interventions resulting in positive impacts on caregivers. Improved programs that meet the needs of caregivers will adequately support caregivers, PWD and reduce financial costs to our healthcare system.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".