Painful leg ulcers: community nurses’ knowledge and beliefs, a feasibility study
Bibliographic record
Abstract
BACKGROUND: Leg ulceration presents a significant health-care problem for patients and it is likely to be associated with pain. Poorly managed persistent pain may lead to insomnia, depression and suicidal ideation. To manage leg ulcer pain effectively, it is necessary first to assess pain, but there is little evidence of this activity in the literature. This study aimed to explore community nurses' knowledge and beliefs about the management of painful leg ulceration. METHOD: A feasibility study surveyed the knowledge and views of a sample of community nurses in the North of England, United Kingdom. Data were collected using a postal questionnaire. RESULTS: A total of 115 completed questionnaires were returned (response rate = 32%). The majority of the respondents were female (n = 102, 91.8%). The nurses in this clinical study claimed to be aware of the pain that patients with leg ulceration may experience. Over 90% of the patients were aware of the key consequences of this pain, including insomnia, depression, social isolation and others. However, over one-third of the nurses reported that they had not received pain management training. Approximately one half reported not assessing pain appropriately. Nurses reported using pain triggers and severity to diagnose aetiology and complications of leg ulceration. CONCLUSION: This study suggested inadequate pain management practices despite the fact that there were a significant number of the nurses who were aware of the associated pain and possible outcomes. Pain assessment was lacking, suggesting a lack of formal pain assessment procedures. They described not using effective pain management techniques and were aware of their need for education in this area.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".