Competencies of specialised wound care nurses: a European Delphi study
Bibliographic record
Abstract
Health care professionals responsible for patients with complex wounds need a particular level of expertise and education to ensure optimum wound care. However, uniform education for those working as wound care nurses is lacking. We aimed to reach consensus among experts from six European countries as to the competencies for specialised wound care nurses that meet international professional expectations and educational systems. Wound care experts including doctors, wound care nurses, lecturers, managers and head nurses were invited to contribute to an e-Delphi study. They completed online questionnaires based on the Canadian Medical Education Directives for Specialists framework. Suggested competencies were rated on a 9-point Likert scale. Consensus was defined as an agreement of at least 75% for each competence. Response rates ranged from 62% (round 1) to 86% (rounds 2 and 3). The experts reached consensus on 77 (80%) competences. Most competencies chosen belonged to the domain 'scholar' (n = 19), whereas few addressed those associated with being a 'health advocate' (n = 7). Competencies related to professional knowledge and expertise, ethical integrity and patient commitment were considered most important. This consensus on core competencies for specialised wound care nurses may help achieve a more uniform definition and education for specialised wound care nurses.
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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 | 0.032 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".