Community-dwelling individuals living with chronic wounds: Understanding the complexity to improve nursing care. A descriptive cohort study
Bibliographic record
Abstract
Background: Chronic disease management is a priority in most healthcare systems. The burden of managing these chronic conditions frequently falls on nurses since the majority of individuals with chronic conditions are treated by nurses in the community or home care setting. Chronic wounds are frequently one of the conditions managed thus were used as an example to illustrate the profile of the population being referred for community care. Fully understanding this population is a first step toward successful chronic disease management and improved care planning for nursing. Objectives: 1) Describe demographic, circumstance-of-living, clinical and wound characteristics of individuals living with a chronic condition; 2) Determine pain, health-related quality of life (HRQoL), function and health outcomes on admission and along the trajectory to healing; and 3) Identify factors to assist health authorities in gathering planning data for chronic disease populations. Design: Secondary data analysis of four major studies using a descriptive/exploratory design. Setting: Care delivered in the community in three Canadian provinces at either a clinic or in home by trained nurses using an evidence-informed protocol. Participants: 735 cognitively intact adults receiving community wound care for a leg ulcer below the knee of venous or venous-mixed etiology. Main Outcome Measure(s): Demographic, clinical, circumstance-of-living, ulcer characteristics and interventions, pain, HRQoL and health outcomes, and healthcare utilization. Main Results: Participants averaged 68 years old, 30% had three or more comorbidities, and many lived alone (37%). Ulcers were present for 11 weeks (median) prior to receiving care and took almost 10 weeks to heal. At baseline 85% reported leg ulcer pain, 53% issues with mobility, 24% issues with washing or dressing, 58% had issues performing usual activities, and one-third reported moderate anxiety or depression. Conclusion: The data illustrate the complexity surrounding individuals receiving community care for a chronic wound and illuminate the challenges faced in planning an effective chronic disease management approach. Implications from this study are relevant to planners, policy-makers and frontline care providers and a number of specific recommendations are offered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".