Beyond Engagement: Realizing Nurses’ Capacity to Lead Sustainable Health Systems
Bibliographic record
Abstract
The health system is a major contributor to Canada's greenhouse gas emissions, largely arising from the ways that care is organized and delivered. Nurses, representing the largest group of regulated healthcare professionals, are experts in the organization and delivery of care, and are uniquely and critically positioned to witness and address the harmful effects of climate crisis. Thus, sustainable health systems cannot be achieved without nurses. Yet, nurses' capacity to lead on issues of climate crisis and sustainability remains underdeveloped. We argue that the nursing profession needs to widely embrace climate crisis as a priority nursing problem and to take visible leadership on this issue. To enable the transformation of the health system toward sustainable and equitable delivery of care, health systems should incorporate a sustainability lens into strategic decision making, and implement and scale up nurse-led models of care. It is time to move beyond "engaging" or even "empowering" nurses to participate in sustainability initiatives. It is time for nurses to lead.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.060 | 0.047 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".