If not us then who?: A focused ethnography exploring caring patterns among planetary health nurses.
Bibliographic record
Abstract
The environment in which nursing occurs is interdependent and dynamic in relation to people and their health circumstances. Planetary health challenges, such as climate change, pollution, deforestation, overfishing, and habitat destruction, are disproportionately experienced in certain geographies and act as threat multipliers to the health, welfare, and security of human and more-than-human species. Nurses, by virtue of their position, are increasingly confronting the health implications of social and economic inequity when people and populations rendered vulnerable struggle to adapt to and mitigate environmental changes. Working near those who suffer, nurses must understand that human health is interrelated with planetary health. This awareness should inform their role as care providers who can develop solutions to face the unprecedented challenges now and in the future. The urgency to protect the environment is reflected among planetary health-conscious nurses who demonstrate a broad nursing perspective that includes caring for those in need, human and more-than-human alike. \n\nThe aim of this study was to interpret questions regarding the journeys, approaches, activities, and priorities of 14 registered nurses actively engaged in planetary health initiatives. A focused ethnographic methodology was employed, which included data from semi-structured interviews, participant observations, and arts-informed self-reflections. This data was analyzed using a reflexive thematic analysis to identify themes related to nurses’ experiences and approaches to planetary health. The results of this research have the potential to inform practice, policy, education, and research within the nursing profession. Additionally, this research serves to highlight the importance of empowering nurses to engage in planetary health initiatives as advocates for social and environmental justice.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".