Nursing interventional package on self-management of COPD patients: In digital era
Bibliographic record
Abstract
Introduction: Chronic Obstructive Pulmonary Disease (COPD) is a leading global health burden, responsible for over 3 million deaths annually. Traditional management approaches have expanded to include self-management interventions, with nurses playing a central role. In the digital era, nursing interventional packages integrating education, skill training, psychosocial support, and digital tools are emerging as key strategies for improving patient outcomes. Methods: A narrative review with systematic elements was conducted following PRISMA principles. Six databases (PubMed, CINAHL, Scopus, Web of Science, Embase, Cochrane) and grey literature were searched for studies published between 2000 and 2025. Eligible studies included adult COPD patients receiving nurse-led self-management interventions, with or without digital integration. Data extraction covered intervention characteristics, outcomes, and effectiveness. Quality appraisal employed Cochrane, Newcastle-Ottawa, CASP, and AMSTAR-2 tools. Results: Out of 3,482 records screened, 132 studies were included. Nurse-led interventions significantly improved self-efficacy, medication adherence, inhaler technique, and quality of life, while reducing hospital readmissions. Digital tools, including tele-nursing and mobile health applications, enhanced accessibility, patient engagement, and continuity of care. Hybrid models (face-to-face plus digital) produced the strongest and most sustainable outcomes. Psychosocial benefits, such as reduced anxiety and improved social support, were frequently reported. Cost-effectiveness analyses demonstrated substantial healthcare savings, though implementation barriers in low- and middle-income countries included digital illiteracy and limited infrastructure. Discussion: Nursing interventional packages are effective in improving COPD self-management, with digital integration amplifying their impact. Future research should explore long-term sustainability, equity in digital access, and advanced technologies. Policymakers should support nurse-led, digitally enabled interventions as cost-effective, patient-centered strategies for chronic disease management.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".