Understanding the impact of dementia and age‐related vision loss on older adults’ participation
Bibliographic record
Abstract
INTRODUCTION: Although a dual diagnosis of dementia and age-related vision loss (ARVL) is common in later life, there is minimal research to describe its impact on participation. As such, the aim of this scoping review was to explore the combined impact of ARVL and dementia on the participation of older adults, with a specific focus on highlighting strategies that help mitigate the impact of ARVL and dementia on participation. METHODS: This study adopted a scoping review methodology based off the Arksey & O'Malley (2005) framework. An exhaustive search of 62 terms across six databases (PubMed, CINAHL, Scopus, EMBASE, Medline, and PsycINFO) was conducted. In addition, the reference lists of included articles, as well as grey literature, including conference proceedings, textbooks, and websites were searched. To be included in the review, each study had to: 1) involve older adults (65+ years old); b) be focussed on the outcome of participation; c) include a dual diagnosis of dementia and ARVL; d) be written in English and e) be available through the university's library database. RESULTS: After a title, abstract, and full-text screen, 13 research articles and 10 grey literature sources that met the inclusion criteria were included in the final review. Following detailed thematic analysis of the empirical and grey literature sources, four themes emerged regarding the impact of combined ARVL and dementia on the participation of older adults including: 1) Managing the pragmatic aspects of a dual diagnosis; 2) Diverse approaches to risk assessment and management; 3) Adopting a multi-disciplinary approach to facilitate care and 4) Using compensatory strategies to facilitate participation. DISCUSSION/CONCLUSION: With the growing number of older adults aging with a dual diagnosis of dementia and ARVL, it is imperative to understand the unique participation needs of this population to develop appropriate and targeted rehabilitation services. Perhaps most pressing is the need for collaborative working relationships to develop across practice areas, such that vision professionals and dementia care professionals could share best practices, knowledge, and skills to improve the quality of care, and support the participation, of older adults aging with this dual diagnosis.
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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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".