Co-Design and Mixed-Methods Evaluation of a Digital Diabetes Education Intervention for Nursing Homes: Study Protocol
Bibliographic record
Abstract
Background: Diabetes is common among nursing home residents, with approximately one in four affected, a figure expected to rise. Despite the complexity of care required, educational support for nursing home staff remains limited. This study will aim to co-design and evaluate a digital intervention to improve staff knowledge, confidence, and practices in diabetes care. Methods: The study will follow a logic model across three workstreams. Workstream 1 (WS1) will inform the model inputs through three phases: (1) a scoping review will be conducted to summarise existing diabetes education initiatives in nursing home settings; (2) approximately 20 semi-structured interviews will be carried out with nursing home staff to explore perceived barriers and supports in delivering diabetes care; and (3) a modified Delphi process involving 50–70 diverse stakeholders will be used to establish educational priorities. Workstream 2 (WS2) will involve co-designing a digital diabetes education intervention, informed by WS1 findings. Co-design participants will include nursing home staff, diabetes professionals, and people living with diabetes or their carers. Workstream 3 (WS3) will consist of a mixed-methods evaluation of the intervention. Pre- and post-intervention questionnaires will assess staff knowledge, confidence, and attitudes. The usability of the intervention will also be measured. Following implementation, focus groups with approximately 32 staff members will be conducted to explore user experiences and perceived impact on resident care. Discussion: This study will address an important gap in staff education and support, aiming to improve diabetes care within nursing home settings through a digitally delivered, co-designed intervention.
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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 | 0.088 | 0.069 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.054 | 0.011 |
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".