Understanding the needs of caregivers of persons with dementia: a scoping review
Bibliographic record
Abstract
BACKGROUND: The number of people living with dementia (PWD) is increasing worldwide, corresponding with an increasing number of caregivers for PWD. This study aims to identify and describe the literature surrounding the needs of caregivers of PWD and the solutions identified to meet these needs. METHOD: A literature search was performed in: PsycInfo, Medline, CINAHL, SCIELO and LILACS, January 2007-January 2018. Two independent reviewers evaluated 1,661 abstracts, and full-text screening was subsequently performed for 55 articles. The scoping review consisted of 31 studies, which were evaluated according to sociodemographic characteristics, methodological approach, and caregiver's experiences, realities, and needs. To help extract and organize reported caregiver needs, we used the C.A.R.E. Tool as a guiding framework. RESULTS: Thirty-one studies were identified. The most common needs were related to personal health (58% emotional health; 32% physical health) and receiving help from others (55%). Solutions from the articles reviewed primarily concerned information gaps (55%) and the education/learning needs of caregivers (52%). CONCLUSION: This review identified the needs of caregivers of PWD. Caregivers' personal health emerged as a key area of need, while provision of information was identified as a key area of support. Future studies should explore the changes that occur in needs over the caregiving trajectory and consider comparing caregivers' needs across different countries.
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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 | 0.018 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".