Commentary on “Diabetes and people with learning disabilities: issues for policy, practice and education”
Bibliographic record
Abstract
Purpose There has been an increase in research exploring the area of intellectual and developmental disabilities (IDD) and diabetes. Despite being described as instrumental to diabetes care for people with IDD, the role and experiences of family carers, such as parents and siblings, are often neglected in this research. However, it is clear that family carers do not feel that they have sufficient knowledge about diabetes. The purpose of this commentary is to extend the content from “Diabetes and people with learning disabilities: Issues for policy, practice, and education (Maine et al., 2020)” and discuss how family carers can feel better supported when caring for someone with IDD and diabetes. Design/methodology/approach This commentary discusses specific efforts such as STOP diabetes, DESMOND-ID and OK-diabetes for people with IDD including family carers. Encouragement is given for health care providers to recommend such programmes to people with IDD and their family carers. It is also suggested that health care providers involve family carers in diabetes care planning and implementation for people with IDD. Findings It is hoped that if changes are made to current diabetes practices and more research with family carers is conducted, diabetes prevention and management for people with IDD will be more successful and family carers can feel more confident in providing support to their loved ones. Originality/value Research exploring the role of family carers in diabetes care for people with IDD and diabetes is very limited. This commentary makes recommendations to help family carers feel better supported in their role. It also provides areas for future research.
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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 | 0.017 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.056 | 0.050 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".