Leadership for Health System Transformation: What's Needed in Canada? Brief for the Canadian Nurses Association's National Expert Commission on The Health of Our Nation – The Future of Our Health System
Bibliographic record
Abstract
What Is ACEn?The Academy of Canadian Executive Nurses represents the voice of nursing leadership in Canada.Founded over 30 years ago, ACEN is now a network of nurses in executive and leadership positions in healthcare, education, research, government, and health and nursing associations.ACEN provides a forum for nurse executives to deal with unique challenges and a network to influence federal policies on several health-related subjects in the interest of better healthcare for Canadians.ACEN would be pleased to collaborate with the CNA's National Expert Commission and other key stakeholders to support and implement emerging recommendations in our organizations and regions and at the national level. Canadian Values and the Leadership ImperativeCanada's healthcare system is fundamental to Canadian culture and identity.Canadians strongly support the Canada Health Act's principles of administration, comprehensiveness, universality, portability and accessibility and are concerned about the long-term sustainability and accountability of our healthcare system (Commission on the Future of Health Care in Canada 2002).
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".