The Value of Nurses Specialized in Wound, Ostomy, and Continence: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To critically appraise peer-reviewed evidence concerning the value, or implied sense of worth or benefit, of nurses specialized in wound, ostomy, and continence (WOC) care. DATA SOURCES: The Preferred Reporting Items for Systematic Reviews and Meta-analyses was used to systematically review current literature in a single database from 2009 to the date of search (July 2019). STUDY SELECTION: The initial search retrieved 2,340 elements; 10 studies were retained following removal of duplicate records, title and abstract reviews, and application of the inclusion/exclusion criteria. DATA EXTRACTION: Literature was graded and critiqued with regard to design and research quality and then synthesized using a narrative approach. DATA SYNTHESIS: Nine values that WOC nurses demonstrate were identified: improved quality of life for patients, teaching and mentoring, cost reduction, improved efficiency, improved wound outcomes, improved incontinence outcomes, advanced treatments, research, and leadership. CONCLUSIONS: Although current studies suggest that there is value in the WOC nurse role, in all areas of the trispecialty, there is a need for high-quality literature with higher-level designs focused on bias reduction.
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 | 0.030 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".