Executive Summary: Debridement: Canadian Best Practice Recommendations for Nurses Developed by Nurses Specialized in Wound, Ostomy and Continence Canada (NSWOCC).
Bibliographic record
Abstract
Debridement is described in the literature as having a high level of clinical risk and may result in patient harm when performed by untrained nurses. As a result, specialized knowledge, skills, and competencies are required to initiate, direct, and perform safe and effective debridement. This executive summary provides an overview of Debridement: Canadian Best Practice Recommendations for Nurses from the Nurses Specialized in Wound, Ostomy and Continence Canada (NSWOCC). The primary objective of these recommendations is to positively influence patient outcomes and enhance safety. The 12 recommendations place the safety of the patient and nurse at the forefront and highlight the educational, competency, certification, preceptor/mentorship, and legal requirements for nurses to initiate, direct, and perform all methods of debridement. We designed these recommendations to be circulated and implemented widely by nurses of various professional levels across the continuum of care and advocate for organizations and government agencies to clearly define debridement in their policies and legislative regulations.
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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.030 | 0.020 |
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".