Bibliographic record
Abstract
Calls for national-level chief nursing officers are over a century old. However, global uptake of these roles and Canadian opportunities for nurses to lead in federal health policy environments have been limited. The absence of such a role in Canada limits connection to global activities, reduces Canadian capacity to participate as a member state in World Health Organization-led nursing activities and, within our borders, leaves the healthcare system without national leadership to coordinate and liaise with senior nurses across our provinces and territories for the benefit of our citizens and systems. In this article, a brief history of global advocacy for state or national level chief nursing officer roles and examples of federal chief nursing officers, in the Canadian context, offers a consideration of the unique contribution of nursing knowledge and leadership to health policy - extending arguments for nursing leadership in the policy arena beyond traditional arguments of strength in numbers or unique claims to caring. Findings from a Canadian national project by the Global Nursing Leadership Institute further illuminate the concrete steps we need to take toward enabling full-spectrum nursing leadership in policy, where policy science, political competency, identification of policy nurses and a revitalization of organizational structures across the country can extend the vision for nursing leadership in policy beyond a single seat at a single table.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.047 | 0.047 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".