Embedding a Global Perspective into Canadian Nursing’s Policy Priorities: Observations from the International Council of Nurses’ 2021 Congress
Bibliographic record
Abstract
Nursing is a global profession, and the COVID-19 pandemic has illustrated just how interconnected and interdependent nursing and health systems are across jurisdictions. The International Council of Nurses (ICN) is a federation of more than 130 national nursing associations and serves as a key policy voice at the global level. Every two years, their congress brings together thousands of nurses and stakeholders to share and disseminate knowledge. Although Canadian presence has historically been strong in these global fora, there is a lack of literature that focuses on discussing the implications of these global discussions on Canadian nurses' policy and advocacy engagement. In this article, we provide a framework of key policy themes as observed during the ICN's 2021 virtual congress. We discuss how these global policy themes align with Canadian nursing, health and public policy priorities and conclude with questions to guide nurses in embedding a global perspective into their research, policy, education and practice initiatives.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.055 | 0.022 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".