Getting to the Heart of the Planetary Health Movement: Nursing Research Through Collaborative Critical Autoethnography
Bibliographic record
Abstract
Humans and more-than-humans experience injustices related to the triple planetary crisis of climate change, pollution, and biodiversity loss. Nurses hold the power and shared Responsibility (Note on Capitalization: Indigenous Scholars resist colonial grammatical structures and recognize ancestral knowledge by capitalizing references to Indigenous Ways of Knowing (Respect, Relations, and Responsibilities are capitalized to acknowledge Indigenous Mi’kmaw Teachings of our collective Responsibilities to m’sit no’ko’maq (All our Relations). Respect for Land, Nature, Knowledge Keepers, Elders, and the names of Tribes, including the Salmon People and sacred spaces, such as the Longhouse, are also denoted with capitals)) to support the health and well-being of each other and Mother Earth. The heart of the Planetary Health movement to address these impacts centers on an understanding of humanity’s interconnection within Nature. As nurses, we seek partnerships with more-than-human communities to promote personal and collective wellness, Planetary Health, and multispecies justice. This article introduces a longitudinal, collaborative autoethnography of our initial engagement with more-than-human communities. In this research, we utilize reflexive photovoice and shared journals to describe our early conversation about this interconnection with three waterways across diverse geographies. This work acknowledges the importance of relational and embodied Ways of Knowing and Being. We invite nurses to embrace the heart of the Planetary Health movement and share these stories with their more-than-human community partners.
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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 | 0.036 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.022 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".