Simulated Wound Care as a Competence Assessment Method for Student and Registered Nurses
Bibliographic record
Abstract
ABSTRACT OBJECTIVE To describe the development and use of a wound care simulation assessing RNs’ and graduating student nurses’ practical wound care competence and to describe observations of participants’ wound care competence. METHODS A descriptive, qualitative design was used. Data were collected in 2019 from 50 healthcare professionals and students using a simulated wound care situation and an imaginary patient case. The simulation was based on a previously developed and tested wound care competence assessment instrument, which included a 14-item checklist that assesses practical wound care competence of chronic wounds. The data were analyzed and described based on the 14 competence areas or as other competencies. RESULTS Participants showed competence in identification of wound infection, debridement, dressing selection, tissue type identification, and consultation. Participants’ shortcomings were related to pain assessment and management, asepsis, offloading, and documentation. Simulation was shown to be a promising tool to assess healthcare professionals’ and students’ practical wound care competence in a safe and standardized situation. CONCLUSIONS This study provided new information about simulation as a method to assess student nurses’ and RNs’ wound care competence. The results could be used in wound care education planning and development in both undergraduate nursing education and continuing education for nursing professionals.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".