Diabetes self-management education (DSME) for older persons in Western countries: A scoping review
Bibliographic record
Abstract
Diabetes mellitus is a chronic metabolic health condition affecting millions globally. Diabetes is a growing concern among aging societies, with its prevalence increasing among those aged 65 and above. Enabling disease self-management via relevant education is part of high-quality care to improve health outcomes and minimize complications for individuals living with diabetes. Successful diabetes self-management education (DSME) programs usually require tailoring for the intended audience; however, there is limited literature about the preferences of older persons in Western countries concerning DSME. As such, a broad overview of DSME for older persons was an identified need. To map the available evidence on DSME for persons aged 65 years and older in Western countries, the JBI methodology for conducting and reporting scoping reviews was used. In this scoping review, we considered all studies about DSME for older persons with T1D and T2D in Western countries where lifestyles, risks, prevention, treatment of diabetes, and approaches to self-management and DSME are similar (e.g., North America, Western and Northern Europe and Australasia). Systematic keyword and subject heading searches were conducted in 10 databases (e.g., MEDLINE, JBI EBP) to identify relevant English language papers published from 2000 to 2022. Titles and abstracts were screened to select eligible papers for full-text reading. Full-text screening was done by four independent reviewers to select studies for the final analysis. The review identified 2,397 studies, of which 1,250 full texts were screened for eligibility. Of the final 44 papers included in the review, only one included participants' understanding of DSME. The education programs differed in their context, design, delivery mode, theoretical underpinnings, and duration. Type of research designs, outcome measures used to determine the effectiveness of DSME, and knowledge gaps were also detailed. Overall, most interventions were effective and improved clinical and behavioural outcomes. Many of the programs led to improvements in clinical outcomes and participants' quality of life; however, the content needs to be adapted to older persons according to their culture, different degrees of health literacy, preference of education (e.g., individualized or group), preference of setting, degree of frailty and independence, and comorbidities. Few studies included the voices of older persons in the design, implementation, and evaluation of DSME programs. Such experiential knowledge is vital in developing educational programs to ensure alignment with this population's preferred learning styles, literacy levels, culture, and needs-such an approach could manifest more substantive, sustained results.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".