Engaging with nature to promote health: new directions for nursing research
Bibliographic record
Abstract
AIM: The aim of this paper is to offer a conceptual framework for nature-based health promotion in nursing and provide related recommendations for future nursing research. BACKGROUND: Empirical data suggest that interaction with nature has direct health benefits. When people attend to outdoor habitats, gardens and other forms of nature, they are more likely to engage in physical activity and other behaviours that improve health. Engaging with nature can even cultivate ecological sensibilities that motivate us to protect the health of our planet. DATA SOURCES: Multidisciplinary theoretical and research publications from 1985 to 2008 were examined in the development of the framework. DISCUSSION: As the health of our planet continues to deteriorate, there is a pressing need for theoretically informed, ethical, sustainable ways of engaging with nature to promote human and environmental health. We adapt principles and socio-ecological thinking from the fields of nursing, health promotion and ecological restoration to delineate the essential elements of the proposed framework. Implications for nursing. Although evidence-based knowledge about nature-based health promotion is not readily used in nursing and health care, its development and application are critical to designing effective strategies to strengthen both human and environmental health. CONCLUSION: Nurses can use nature-based health promotion concepts to work with citizens, health practitioners and policymakers to explore and optimize reciprocal, health promoting relationships among humans and the natural environment. To the extent that nurses integrate nature-based health promotion into their research efforts, we can expect to contribute meaningfully to both environmental and human health in communities across the globe.
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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 | 0.051 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.019 | 0.038 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".