Is Living with Persons with Dementia and Depression Correlated to Impacts on Caregivers? A Scoping Review
Bibliographic record
Abstract
Abstract Caregivers of persons with dementia and depression experience adverse effects associated with their role. The aim of this scoping review was to identify the challenges faced by caregivers of people with dementia and depression, along with interventions to support them. The MEDLINE®, Embase and PsycINFO databases were searched using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. Grey literature was assessed using the Canadian Agency for Drugs and Technologies in Health’s Gray Matter tool. The population consisted of caregivers of people with dementia and depression; the concept was to identify the negative impacts that caregivers experience and whether there are interventions to reduce them; the context was any study design targeting family or friends who were caregivers. A total of 12,835 citations were identified; 139 studies were included. Dementia and depression have variable impacts on outcomes experienced by caregivers, including burden/strain (n = 52), depression (n = 27), distress (n = 53), quality of life (n = 5) and health/well-being (n = 9). Pharmacological and non-pharmacological interventions have mixed effects. This study is important considering that depression in people with dementia is associated with caregiver distress. The use of a variety of non-pharmacological interventions could be beneficial to the latter.
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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 | 0.009 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".