Message from the Chief Executive Officer
Bibliographic record
Abstract
As we approach the completion of the Canadian Best Practice Recommendations in Wound Care for People Who Use Drugs: A Harm Reduction Approach, I am reminded of the significant contributions that volunteers make to our organization and the communities we support. Focusing on harm reduction teaches us to meet people where they are without judgement and to deliver the best possible wound, ostomy and continence care. Thank you to Lili Berescu and Priyanka Jani for bringing this project forward and seeing it to completion. Your leadership is evident in all you do. I would also like to thank the Harm Reduction Nurses Association and The Ontario Network of People Who Use Drugs for partnering with us to bring both clinical and lived experience to these best practice recommendations. Volunteers drive positive change and promote inclusion in wound, ostomy and continence care. Your dedication and compassion contribute significantly to the strength of our organization and enhance the experiences of individuals across our diverse health care system. At a time when there is so much uncertainty, you help to stabilize Nurses Specialized in Wound, Ostomy and Continence Canada (NSWOCC) through your participation, guidance, and inspiration. On behalf of the NSWOCC team, I want to express my deepest gratitude to each of you for your contributions to our association, regardless of the extent of your involvement. All contributions, whether as a volunteer member of the NSWOCC board, a Core Program Leader, a participant in best practice recommendation development, or as support for various NSWOCC programs and services, are recognized and highly valued. Together, we continue to create and foster an inclusive environment for people with wound, ostomy and continence needs.
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.094 | 0.054 |
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".