Planetary Health in Nursing: A Scoping Review
Bibliographic record
Abstract
AIM: To map the extent of the use of the term 'planetary health' in peer-reviewed nursing literature. DESIGN: Scoping Review. DATA SOURCES: CINAHL, ProQuest Nursing & Allied Health Premium, MEDLINE, APA PsycINFO, ProQuest Dissertations & Theses and Web of Science were searched in January and February 2024 for English and French-language publications. A follow-up search was conducted on 10 June 2024 to determine if additional literature was published. REVIEW METHODS: A scoping review was conducted using the Arksey and O'Malley methodology for scoping reviews. To be included the article had to explicitly use the term 'planetary health' and 'nursing' or 'nurses'. RESULTS: Sixty-eight articles met the criteria for the scoping review and were included in this review, with the majority published between 2017 and 2024. Predominant literature included discussion papers, commentaries and editorials. A lack of original research is apparent. Most of the publications were calls to action for nurses to advance planetary health in nursing education, practice, research and advocacy work. CONCLUSIONS: Literature confirms that planetary health is a recent and an important topic in nursing, and nurses have a well-documented role to play in planetary health, given the numerous calls to action in nursing leadership, education, practice and research. There is a need to publish the essential work nurses are doing in planetary health in various nursing domains. IMPACT: This scoping review revealed a clear and urgent call to action for nurses to address planetary health. Given this finding, nurses have a responsibility to advocate for a planetary health approach in the profession and take action to contribute to planetary health through education, research, practice and advocacy. NO PATIENT OR PUBLIC CONTRIBUTION: Not applicable, as no patients or public were involved.
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 | 0.041 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.048 | 0.040 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.003 |
| 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".