Getting to the Heart of the Planetary Health Movement: Nursing Research Through Collaborative Critical Autoethnography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it