Just‐relations and responsibility for planetary health: The global nurse agenda for climate justice
Bibliographic record
Abstract
There is an urgent call for nurses to address climate change, especially in advocating for those most under threat to the impacts. Social justice is important to nurses in their relations with individuals and populations, including actions to address climate justice. The purpose of this article is to present a Global Nurse Agenda for Climate Justice to spark dialog, provide direction, and to promote nursing action for just-relations and responsibility for planetary health. Grounding ourselves within the Mi'kmaw concept of Etuaptmumk (two-eyed seeing), we suggest that climate justice is both call and response, moving nurses from silence to Ksaltultinej (love as action). We review the movement for climate justice in nursing, weaving between our own stories, our relations with Mi'kmaw ways of knowing, and the stories of the movement, with considerations for the (w)holistic perspectives foundational to nursing's metaparadigm of person, environment, and health. We provide a background to the work of the Global Nurse Agenda for Climate Justice steering committee including their role at the 26th United Nations Climate Change Conference in Glasgow, 2021, and share our own stories of action to frame this agenda. We accept our Responsibility for the challenges of climate justice with humility and invite others to join us.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".