Bibliographic record
Abstract
Each year the NSWOCC President has the privilege of recognizing an NSWOC who has exemplified leadership in the wound, ostomy and continence specialty. The President's Award goes beyond job titles. The award goes to someone who has worked tirelessly, often behind the scenes, to advance our mission, support our community and elevate our specialty in meaningful and lasting ways. This year the recipient is truly a champion of evidence informed practice. With over twenty years of research and dozens of publications from peer reviewed articles to textbook chapters, to textbooks, this individual is leading change and knowledge development throughout our specialty and profession. This year's Awardee has been an ambassador for the association at an international level. He has led our association’s Research Practice Core Program and assisted with the literature searches for all best practice recommendation documents over the past five years. He has served on the International Skin Tear Advisory Panel and helped found the Canadian Pressure Injury Advisory Panel. This individual has consistently demonstrated the NSWOCC values of excellence, leadership and collaboration. I am proud to say that I have personally experienced the mentorship that this individual provides first as a PhD supervisor and now as a colleague. I now have the honour of awarding Dr. Kevin Woo with the 2025 NSWOCC President's Award.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.179 | 0.099 |
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".