A Scoping Review of Dementia and Diabetes Co-Morbidity Care
Bibliographic record
Abstract
Diabetes mellitus is a common co-morbidity with dementia. Diabetes is usually a self-managed condition requiring an individual to have a high level of cognitive functioning; hence dementia makes managing diabetes very challenging for the individual with dementia and their caregivers. The purpose of this literature review was to synthesise what is currently known about diabetes and dementia co-morbidity care outcomes. Online databases (AMED, CINAHL, PROQUEST, EBM Reviews, TRIP, Medline and PsycINFO) were searched for the period 2012 – 2023, from which we selected 27 publications. Of the 27 publications 8 were literature reviews/expert discussion of the literature, 1 audit, 1 case report, 5 cohort studies, 2 cross sectional studies, 4 mixed methods studies, 2 realist reviews, 1 longitudinal observation study and 3 qualitative studies. Selected literature was from the UK, USA, Canada, Japan, Austria, Germany, France and Poland. Five themes permeated this literature: key principles of care, challenges of diabetes management as a consequence of dementia, complexity of care, quality of care and workforce issues. This review highlights the complexity of care for those with co-morbid diabetes and dementia, which are both progressive diseases which change over time. There is a need to develop the underpinning evidence base in order to provide guidelines for best practice, to support staff to deliver appropriate care to people living with this co-morbidity
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".