A scoping review of post-diagnostic dementia supports for people with intellectual disability
Bibliographic record
Abstract
Objectives: People with intellectual disability, particularly people with Down syndrome, are at an increased risk for early-onset dementia, in comparison to people without an intellectual disability. The aim of this review was to scope the current landscape of post-diagnostic dementia supports for people with intellectual disability. Method: A systematic search of five electronic databases (CINAHL, Medline, PsycArticles, PsycInfo and Web of Science) was conducted for this scoping review. Results were screened independently by two reviewers, with a third reviewer for arbitration where necessary. Results: Forty-two studies met the inclusion criteria, and relevant information was extracted. The articles included focussed on the experiences of people with intellectual disability and dementia, as well as the role of carers, family members and staff. Key themes included ageing in place, environmental supports for people with intellectual disability and dementia, dementia-specific interventions and therapies, as well as the feasibility of these interventions. Besides the studies that focussed on these themes, other studies focussed on staff training and family supports. Conclusion: This review highlights the importance of implementing timely and appropriate post-diagnostic supports for people living with intellectual disability and dementia. More controlled trials are required on post-diagnostic dementia supports for people with intellectual disability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".