Bibliographic record
Abstract
A spotlight on our ostomy specialty The Canadian Journal of Wound, Ostomy and Continence (CJWOC) has always sought out, and continues to seek out, articles focused on adult and paediatric wound, ostomy and continence matters. This issue is dedicated exclusively to our ostomy specialty, reflecting our continued commitment to advancing knowledge in this area. Authors Heerschap, Butt, Franco, Hughes, Musa, McCauley, and colleagues contributed 2 of the 5 scoping reviews that form part of the Ostomy Assessment Systematic Integration of Studies (OASIS) study, which is a large review focused on the assessment of persons living with an ostomy. The first article examines available assessment tools, while the second explores psychological and social factors related to living with an ostomy. Readers are encouraged to review both articles, as they outline gaps and priorities, define the scope and nature of the evidence, and clarify concepts important to nursing scholarship and practice. On a more personal level, we sincerely thank our CEO, Catherine Harley, for over twenty years of exemplary leadership to Nurses Specialized in Wound, Ostomy and Continence Canada. Her unwavering support has been instrumental in advancing our journal from its origins as a newsletter to its current status as an open-access, peer-reviewed publication. Thank you for your incredible dedication and invaluable contributions. Wishing you endless joy, relaxation and exciting new adventures in your next chapter!
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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 | 0.011 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.021 | 0.019 |
| Insufficient payload (model declined to judge) | 0.038 | 0.041 |
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".