Wound management provided by advanced practice nurses: a scoping review protocol.
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to examine the current state of the literature regarding wound care provided by advanced practice nurses globally. Specifically, this review will examine the similarities and differences in the wound care practice of advanced practice nurses, including nurse practitioners, clinical nurse specialists, and advanced practice registered nurses.\nINTRODUCTION: Advanced practice nurses have graduate education and advanced scope of practice. The addition of advanced wound care training provides unique opportunities for advanced practice nurses to provide wound care.\nINCLUSION CRITERIA: This review will consider advanced practice nurses who are nurse practitioners or registered nurses with graduate education and advanced training (certification/education) in wound care. The wound care can be provided independently or as a part of a team, in any setting.\nMETHODS: The proposed review will be conducted in accordance with the JBI methodology for scoping reviews. The databases searched will include MEDLINE, CINAHL, ProQuest Nursing and Allied Health, Cochrane Database of Systematic Reviews, and Scopus. To reflect changes in advanced practice nursing scope of practice, searches will be limited to articles published from 2011. Articles in languages other than English will be translated. Titles and abstracts will be independently reviewed by two reviewers, and relevant sources will be retrieved in full and reviewed. Any disagreements will be resolved through discussion or with an additional reviewer. The similarities and differences in wound care practice (type of wound, practice setting, treatments) will be extracted using a data extraction tool. Any modifications will be detailed in the scoping review. Extracted data will be presented in a descriptive format.
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 | 0.103 | 0.071 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.058 | 0.014 |
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".