BlueHealth: a study programme protocol for mapping and quantifying the potential benefits to public health and well-being from Europe's blue spaces
Bibliographic record
Abstract
INTRODUCTION: Proximity and access to water have long been central to human culture and accordingly deliver countless societal benefits. Over 200 million people live on Europe's coastline, and aquatic environments are the top recreational destination in the region. In terms of public health, interactions with 'blue space' (eg, coasts, rivers, lakes) are often considered solely in terms of risk (eg, drowning, microbial pollution). Exposure to blue space can, however, promote health and well-being and prevent disease, although underlying mechanisms are poorly understood. AIMS AND METHODS: The BlueHealth project aims to understand the relationships between exposure to blue space and health and well-being, to map and quantify the public health impacts of changes to both natural blue spaces and associated urban infrastructure in Europe, and to provide evidence-based information to policymakers on how to maximise health benefits associated with interventions in and around aquatic environments. To achieve these aims, an evidence base will be created through systematic reviews, analyses of secondary data sets and analyses of new data collected through a bespoke international survey and a wide range of community-level interventions. We will also explore how to deliver the benefits associated with blue spaces to those without direct access through the use of virtual reality. Scenarios will be developed that allow the evaluation of health impacts in plausible future societal contexts and changing environments. BlueHealth will develop key inputs into policymaking and land/water-use planning towards more salutogenic and sustainable uses of blue space, particularly in urban areas. ETHICS AND DISSEMINATION: Throughout the BlueHealth project, ethics review and approval are obtained for all relevant aspects of the study by the local ethics committees prior to any work being initiated and an ethics expert has been appointed to the project advisory board. So far, ethical approval has been obtained for the BlueHealth International Survey and for community-level interventions taking place in Spain, Italy and the UK. Engagement of stakeholders, including the public, involves citizens in many aspects of the project. Results of all individual studies within the BlueHealth project will be published with open access. After full anonymisation and application of any measures necessary to prevent disclosure, data generated in the project will be deposited into open data repositories of the partner institutions, in line with a formal data management plan. Other knowledge and tools developed in the project will be made available via the project website (www.bluehealth2020.eu). Project results will ultimately provide key inputs to planning and policy relating to blue space, further stimulating the integration of environmental and health considerations into decision-making, such that blue infrastructure is developed across Europe with both public health and the environment in mind.
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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 | 0.088 | 0.065 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.117 | 0.036 |
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".