The process of developing best practice guidelines for nurses in Ontario: risk assessment and prevention of pressure ulcers.
Bibliographic record
Abstract
Linking practice to current evidence-based wound care guidelines is a challenge for healthcare professionals, especially because of the quantity of wound care guidelines available. In 1999, the Registered Nurses Association of Ontario, Canada, with funding from the Province of Ontario's Ministry of Health and Long Term Care, established a process for the development and implementation of 17 best practice guidelines to support nurses using evidence-based practice. Four of the 17 guidelines pertain to wound care. The consensus development, pilot testing, and evaluation process of one of the guidelines, Risk Assessment and Prevention of Pressure Ulcers in Adults, is described. Following a comprehensive and systematic search for existing guidelines, a formal quality appraisal of five selected guidelines, decisions for adoption and/or adaptation of best practice recommendations, and stakeholder feedback on the draft guidelines, a pilot implementation testing of the guidelines was conducted. In early 2002, the nursing best practice guideline was disseminated through conferences, publications, and the Registered Nurses Association of Ontario website www.rnao.org.
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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 | 0.124 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".