Rehabilitation Services for Young-Onset Dementia: Examples from High- and Low–Middle-Income Countries
Bibliographic record
Abstract
The WHO Dementia Global Action Plan states that rehabilitation services for dementia are required to promote health, reduce disability, and maintain quality of life for those living with dementia. Current services, however, are scarce, particularly for people with young-onset dementia (YOD). This article, written by an international group of multidisciplinary dementia specialists, offers a three-part overview to promote the development of rehabilitation services for YOD. Firstly, we provide a synthesis of knowledge on current evidence-based rehabilitative therapies for early-onset Alzheimer's disease (EOAD), behavioural variant frontotemporal dementia (bvFTD), primary progressive aphasia (PPA), and posterior cortical atrophy (PCA). Secondly, we discuss the characteristics of rehabilitation services for YOD, providing examples across three continents for how these services can be embedded in existing settings and the different roles of the rehabilitation multidisciplinary team. Lastly, we conclude by highlighting the potential of telehealth in making rehabilitation services more accessible for people with YOD. Overall, with this paper, we aim to encourage clinical leads to begin introducing at least some rehabilitation into their services, leveraging existing resources and finding support in the collective expertise of the broader multidisciplinary dementia professional community.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".