Planetary health and mental health nursing: What will you do?
Bibliographic record
Abstract
Relational practice is the foundation of mental health nursing. Being in relationship with the people we work with and for is central to our work, but have mental health nurses also considered their relationship with the planet? In 2020, a joint statement was signed by Australian health groups, calling on the government to recognize climate change in future health policy developments (ANMF, 2020). Internationally, the United Nations Sustainable Development Goals released in 2015 is described as a shared blueprint for peace and prosperity for people and the planet (United Nations, 2015). The list of calls to action is long, global and urgent. As advocates for human health and well-being, nurses have an opportunity to lead adaptation and mitigation efforts to promote planetary health. If we are to improve the relationship between nurses and the planet we need to consider, how we relate to the planet. This special edition brings together papers that will add to this urgent conversation and call to action.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".