Impact of Chronic Wounds on Quality of Life: A Review from the Perspective of Humanized Care
Bibliographic record
Abstract
Introduction: Chronic wounds are characterized by slow healing, difficult scarring, and duration of more than three months. They not only affect the body physically, but also have an emotional, social, and psychological impact on those who suffer from them. The consequences include pain, foul odor, excessive exudate, social exclusion, and impaired self-esteem, which significantly reduce health-related quality of life (HRQoL).Development: Through an international, national, and local literature review, it was found that people with chronic wounds faced functional, emotional, and economic difficulties. In Canada, Brazil, and Colombia, limitations in performing basic activities, dependence on caregivers, and a high emotional burden due to persistent symptoms were reported. The predominant sociodemographic characteristics included women over 60 years of age, with low educational attainment, minimal income, and no partner. In the theoretical framework, humanistic nursing theory was integrated, promoting a holistic approach to care, centered on empathy and patient accompaniment, recognizing their individuality and social environment.Conclusions: The study concluded that chronic wounds significantly compromised several dimensions of HRQoL. Pain was the most frequent symptom, and social exclusion was a common consequence. The need for interdisciplinary care that considers not only physical treatment but also emotional and social support was reaffirmed. Humanized care was established as a key strategy for improving patients' experience and overall well-being.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".