Age-appropriate services for people diagnosed with young onset dementia (YOD): a systematic review
Bibliographic record
Abstract
BACKGROUND: Literature agrees that post-diagnostic services for people living with young onset dementia (YOD) need to be age-appropriate, but there is insufficient evidence of 'what works' to inform service design and delivery. OBJECTIVE: To provide an evidence base of age-appropriate services and to review the perceived effectiveness of current interventions. METHODS: We undertook a systematic review including all types of research relating to interventions for YOD. We searched PubMed, CINHAL Plus, SCOPUS, EBSCO Host EJS, Social Care Online and Google Scholar, hand-searched journals and carried out lateral searches (July-October 2016). Included papers were synthesised qualitatively. Primary studies were critically appraised. RESULTS: Twenty articles (peer-reviewed [n = 10], descriptive accounts [n = 10]) discussing 195 participants (persons diagnosed with YOD [n = 94], caregivers [n = 91] and other [n = 10]) were identified for inclusion. Services enabled people with YOD to remain living at home for longer. However, service continuity was compromised by short-term project-based commissioning and ad-hoc service delivery. CONCLUSION: The evidence on the experience of living with YOD is not matched by research and the innovation needed to mitigate the impact of YOD. The inclusion of people with YOD and their caregivers in service design is critical when planning support in order to delay institutional care.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".