Global Leadership Priorities for Canadian Nursing: A Perspective on the ICN 24th Quadrennial Congress, Durban, South Africa
Bibliographic record
Abstract
We had the privilege of joining over 5,000 nurses attending the 24th Congress of the International Council of Nurses, held for the first time on the African continent in Durban, South Africa. The Congress inspired us to reflect on how leadership and policy directions in Canadian nursing resonate with global health challenges and opportunities. Dynamic plenary speakers from African countries inspired the conference theme: Leading Change--Building Healthy Nations. Ensuing discussions signalled shifting priorities and urgent implications for nursing leadership and programs of research in Canada and worldwide, in areas of primary healthcare renewal, nursing health human resources sustainability and health interventions for the achievement of the United Nations Millennium Development Goals (MDGs) (United Nations 2009; WHO 2008). Sharing challenges with nurses worldwide, Canadian nurses are privileged with the resources to address these challenges (CNA 2008; WHO 2008). Our experience at the Congress prompted the question: How must Canadian nurses reshape leadership priorities and agendas not only in the Canadian context, but also in the mutual interests of health for all? Reflecting upon the themes of the Congress and the leadership role of Canadian nurses, we identify three interconnected priorities: Invest our hearts, souls and resources in primary healthcare renewal. Grapple with the complexity of an equitable and sustainable global nursing human resources system. Ensure a lens of social justice through leadership, research and education for the achievement of the MDGs.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.066 | 0.015 |
| Scholarly communication | 0.021 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.014 | 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".