Scoping Review of Clinical Outcomes Related to Advanced Training in Wound Care.
Bibliographic record
Abstract
INTRODUCTION: There are different levels of wound education which exist amongst healthcare providers treating wounds. It is unknown if advanced wound training can lead to improved clinical outcomes. PURPOSE: To review and summarize existing literature focused on the impact of different healthcare professionals with advanced wound care training and the associated effect of clinical outcomes. MATERIALS AND METHODS: The methods used to conduct this scoping review are based on the methodological framework developed by Arksey and O'Malley. An electronic search was performed by independent reviewers using Scopus, CINAHL, PubMed, Google, and EWMA. Consensus decision-making amongst the reviewers resulted in relevant final articles being selected for review. RESULTS: In the literature, there is no universally accepted definition for advanced training in wound care. Seven of the eight selected articles focused on nurses with a specialization in wound healing and their impact on wound healing outcomes. The five main themes identified were wound improvement, cost savings, influence on other nurses, wound recurrence rate, and advanced education. CONCLUSION: A minimum level of advanced training or education would be beneficial to ensure consistency in the provision of advanced wound care by professionals practicing wound care.
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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 | 0.048 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.037 | 0.038 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".