Best Evidence Summary for Management of Older People With Type 2 Diabetes Mellitus Using ‘Internet Plus Nursing Services’
Bibliographic record
Abstract
AIM: To evaluate and summarise the evidence for the management of older people with Type 2 diabetes mellitus (T2DM) using 'Internet Plus nursing services (IPNS)'. DESIGN: This study was conducted as an evidence summary, adhering strictly to the evidence summary reporting standards established by Fudan University Center for Evidence-based Nursing. METHODS: We systematically searched for the best available evidence pertaining to the management of older people with T2DM using the IPNS. The literature types encompassed clinical guidelines, expert consensuses, systematic reviews, evidence summaries and original research studies. DATA SOURCES: In order to gather pertinent information, we conducted a comprehensive search across various databases, including UpToDate, BMJ Best Practice, Joanna Briggs Institute, Guidelines International Network, National Institute for Health and Care Excellence, Registered Nurses Association of Ontario, Scottish Intercollegiate Guidelines Network, the Cochrane Library, PubMed, Web of Science, Yi Maitong Guidelines Network, SinoMed, CNKI, WanFang database, Chinese Biomedical Literature Database and China Science and Technology Journal Database. The search spanned from the inception of each database up to July 2023, ensuring an extensive coverage of relevant resources. RESULT: After rigorous screening and evaluation, our study ultimately identified 19 articles with high-quality research outcomes. These articles consisted of three guidelines, two expert consensus documents, three systematic reviews and eleven original research studies. Through collaborative and in-depth discussions, we extracted and synthesised 27 pieces of evidence related to the application of the IPNS to enhance the T2DM management for older people. We categorised the evidence into five primary themes: mobile terminal design, team building, health education design, interaction and social support, and information feedback. CONCLUSION: In designing the IPNS for older people with T2DM, utmost attention should be paid to the intricacies of Internet module development preceded by comprehensive guidance. It is imperative to establish multidisciplinary teams to oversee the curation of patient educational content, ensuring its relevance and effectiveness. Leveraging Internet-based information feedback mechanisms is crucial for fostering peer support, assisting in blood glucose control, enhancing self-management capabilities and ultimately improving overall quality of life. Healthcare professionals are supposed to customise the IPNS for this vulnerable population, taking into account institutional resources and local contextual realities. FOR THE PROFESSIONAL FIELD AND PATIENT CARE IMPLICATIONS: It is highly recommended that clinical medical personnel worldwide adhere to the evidence-based recommendations for better care of older people with T2DM using the IPNS. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution was incorporated in this study. IMPLICATIONS FOR PRACTICE: Our study provides structured ideas for the application of IPNS in T2DM management, provides practicable new ways for IPNS to improve self-management of older people with T2DM, and encourages the provision of Internet-based nursing services based on the needs and characteristics of older people, and jointly solves the challenges of T2DM management in order to enhance the effectiveness of nursing services.
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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 | 0.020 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".