Quality criteria of nature-based interventions in healthcare facilities: a scoping review

Introduction

Implementing integrated nature-based interventions that simultaneously serve human health and the restoration of biodiversity in healthcare facilities is considered a promising strategy. As an emerging field of research and practice in healthcare, identification of quality criteria is necessary to support desired outcomes related to biodiversity, human health and intervention processes. This study is part of a larger research project in collaboration with the Flemish Agency of Nature and Forest in Belgium.

Methods

A scoping review was conducted in accordance with the Joanna Briggs Institute methodology for scoping reviews, in PubMed, Medline, Web of Science and Scopus. A step-by-step tabular screening process was conducted to identify relevant studies and reviews of nature-based interventions, published in English between January 2005 and April 2023. A qualitative content analysis was conducted and the results were then presented to the project steering group and a panel of stakeholders for refinement.

Results

After filtering on the eligibility criteria, and with focus on healthcare facilities, 14 articles were included in this study. A preliminary nature-based interventions quality framework with a set of quality indicators has been developed.

Discussion

When designing integrated nature-based interventions, a needs analysis of users and the outdoor environment should be conducted. Next, the integration of a One Health and biodiversity perspective and the application of a complex intervention framework, could support the quality of the design and implementation of nature-based interventions in healthcare facilities and facilitate their assessment. In future work, more rigorous research into the design and implementation of integrated nature-based interventions is needed to test and refine the quality criteria in practice.

Prediction uncertainty of environmental change effects on temperate European biodiversity

Observed patterns of species richness at landscape scale (gamma diversity) cannot always be attributed to a specific set of explanatory variables, but rather different alternative explanatory statistical models of similar quality may exist. Therefore predictions of the effects of environmental change (such as in climate or land cover) on biodiversity may differ considerably, depending on the chosen set of explanatory variables. Here we use multimodel prediction to evaluate effects of climate, land-use intensity and landscape structure on species richness in each of seven groups of organisms (plants, birds, spiders, wild bees, ground beetles, true bugs and hoverflies) in temperate Europe. We contrast this approach with traditional best-model predictions, which we show, using cross-validation, to have inferior prediction accuracy. Multimodel inference changed the importance of some environmental variables in comparison with the best model, and accordingly gave deviating predictions for environmental change effects. Overall, prediction uncertainty for the multimodel approach was only slightly higher than that of the best model, and absolute changes in predicted species richness were also comparable. Richness predictions varied generally more for the impact of climate change than for land-use change at the coarse scale of our study. Overall, our study indicates that the uncertainty introduced to environmental change predictions through uncertainty in model selection both qualitatively and quantitatively affects species richness projections.