Co-Design and Mixed-Methods Evaluation of a Digital Diabetes Education Intervention for Nursing Homes: Study Protocol
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it