Geographical inequalities in dementia diagnosis and care: A systematic review
Bibliographic record
Abstract
BACKGROUND: People with dementia can be disadvantaged in accessing health and social care services for diagnosis and care depending on where they live (including rural vs suburban vs. urban; postcode; country). Without an existing comprehensive synthesis of the evidence to date, the aim of this systematic review was to explore the evidence on geographical inequalities in accessing services for dementia diagnosis and care. METHODS: Five databases were searched in June 2024, including studies conducted in any country, published from 2010 onwards, and in English or German. Titles and abstracts, and then full texts, were screened by at least two reviewers each. Any discrepancies were resolved in discussion with a third reviewer. Data were extracted by two researchers and synthesised narratively. RESULTS: From 1321 studies screened and 49 full texts read, 32 studies were included in the final review. Most studies were conducted in the US, followed by the UK. Geographical inequalities in dementia are most often evidenced in relation to availability and suitability of services in different regions within a country, or a lack thereof. People with dementia residing in rural areas often experience challenges in receiving a timely diagnosis and accessing health and social care. No research has addressed geographical inequalities in accessing residential care. Innovative models on improving efficiency and quantity of diagnosis rates in rural Canada and Australia emerged. CONCLUSIONS: Health and social care services in rural areas need to be increased and made more suitable to the needs of people with dementia. More research needs to explore inequalities experienced by people with rarer forms of dementia. National strategies to overhaul the health and social care system need to focus on the rurality issue and recommend strategies to improve service access.
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 | 0.011 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".