Informal caregiving for people with dementia and hearing or vision impairment: A systematic review
Bibliographic record
Abstract
Caregivers of individuals with dementia often face the dual challenge of managing dementia alongside sensory impairments (hearing and/or vision loss). Despite the significant burden, this issue remains underexplored, with existing research largely overlooking the impact of sensory impairments on caregivers' experiences. This systematic review examines the challenges and needs of caregivers managing both conditions. A comprehensive literature search across three databases identified 12 studies published before May 2024 in five countries. The review revealed that caregivers face increased challenges when managing both dementia and sensory impairments. Unmet needs, particularly in accessing tailored support and assistive technologies, were prevalent. Multidisciplinary care and interventions are crucial to address both cognitive and sensory needs. To improve the quality of life for caregivers and sensory impaired dementia patients, comprehensive support systems, enhanced caregiver education, and better access to affordable assistive technologies are essential. Addressing these disparities is critical for providing effective care. HIGHLIGHTS: This review focuses on informal caregiving for people with dementia and sensory loss. Caregivers face increased challenges due to managing both dementia and sensory impairments. There are additive implications of caring for people with dual impairments. Further research is needed on interventions to support this caregiver population.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".