The use of clinical simulation in wound care education for nurses: a scoping review protocol
Bibliographic record
Abstract
Many nurse educators consider simulation a valuable tool to supplement and augment learning due to current shortages of clinical placements. Wound care is integral to nursing practice yet many students and practicing nurses experience difficulties in securing sufficient learning opportunities or experience at the undergraduate level to feel competent in providing it. Emerging evidence supports simulation as a promising intervention to facilitate student learning in wound care, building nurses' confidence and competence in providing evidence-based wound care. OBJECTIVE: To understand how clinical simulation is being used to educate nurses about wound assessment and management, and to explore the impact of clinical simulation on learning outcomes, including knowledge, attitudes, confidence, and skills related to wound care. INCLUSION CRITERIA: Inclusion criteria include studies of nursing students and nurses, simulation educational interventions, and learning outcomes related to wound care evaluated by any measures. Any studies that do not fit these criteria will be excluded. METHODS: Databases to be searched include PubMed/MEDLINE, CINAHL, ERIC, SciELO up to February 2022. Studies in English with a date limit of 2012 to 2022 will be included. Search results will be imported into Covidence and screened by two independent reviewers, first based on the title and abstract and then full text. Data will be extracted with a novel extraction tool developed by the reviewers and then synthesised and presented in narrative, tabular, and/or graphical forms. DISSEMINATION: The finished scoping review will be published in a scientific journal once analysis is completed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.090 | 0.073 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.057 | 0.012 |
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".