A modular approach to diabetes structured education: Effects on patient knowledge, self-efficacy, self-management and patient experience in diabetic kidney disease
Bibliographic record
Abstract
Diabetic kidney disease (DKD) is a serious chronic complication of diabetes, associated with increased risk of cardiovascular disease, end stage kidney disease and mortality. Intensive management, incorporating dietary and lifestyle changes with pharmacological agents, has been shown to reduce associated risks of DKD. This requires multiple self-management (SM) actions to optimise risk factors including diabetes, hypertension and hyperlipidaemia. Diabetes structured education (DSE) is integral to diabetes management and research shows DSE is beneficial to knowledge, SM activities, and diabetes control (Dekain et al., 2009; Speight et al., 2010). However, little evidence exists in DSE focused on DKD, despite the increased risk of mortality associated with the condition and NICE guidelines (NICE, 2008; NICE, 2003) encouraging education to optimise management of diabetes and kidney disease. The aim of the research is to determine whether complication-specific DSE for DKD has an impact on SM, self-efficacy (SE), and knowledge related to DKD, and to identify what effect education has on participants. A mixed method approach, combining quantitative questionnaires and semi-structured qualitative interviews was utilised. A standalone education module specifically for adults with DKD was provided for participants, tailored to the needs of this distinct group. A single education module demonstrated positive changes in SM activities, specifically seeking information, asking questions regarding biomedical results and following suggestions to alter dietary and exercise habits. Improvements were also seen in knowledge related to DKD. Significant positive correlations were demonstrated between SM and SE outcomes related to seeking support and discussing worries with family and friends. Qualitative results identified that social support can have a negative or positive impact on participants depending on the nature of the support. It was also found that participants felt healthcare professionals did not inform them of their biomedical results. An education module specifically for DKD allows the information to be tailored to meet the needs of participants to a greater extend, which is in keeping with NICE guidelines (NICE, 2003). A single education session had a positive impact on participants demonstrated by improvements in DKD knowledge, SE and increased engagement in SM activities. Healthcare professionals can improve partnership with patients through the sharing of, and the significance of, biomedical information. This could have a benefit in reducing the health burden of DKD considering its morbidity and mortality risk.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".