Is University Nursing Education in Canada Taking the Lead in a World Focused on Sustainable Development?
Bibliographic record
Abstract
Global health is widely being adopted by universities and higher education institutions in Canada and around the world. The current global climate has given rise to an emphasis on the necessity of global health education for nurses. Nursing educators as well as nursing students are seeking guidance as they integrate global health as part of their learning, teaching, research, and practice. In September 2015, the member states of the United Nations adopted the sustainable development goals (SDGs): 17 goals to end poverty, protect the environment, and ensure health and well-being for all. These 17 goals will guide the world’s development agenda for the next 15 years. Canadian universities, especially nursing faculties/schools are uniquely placed to help implement the SDGs, particularly goals 3 and 4, which focus on good health and well-being and quality education. Little has been done in understanding universities and in particular nursing’s overall commitment to achieving these 17 goals. Nursing is the largest health care provider group and it is critical to understand our educational responsibilities in attaining the SDGs. The purpose of this paper is to share findings from a study which examined Canada’s largest nursing faculties’/schools of nursing’s mission statements and strategic plans, and to discuss how these mandates align with the achievement of the SDGs.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".