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Nature contact and health risk Behaviours: Results from an 18 country study

2025· article· en· W4410328044 on OpenAlexfundno aff
Leanne Martin, Mathew P. White, Sabine Pahl, Jon May, John Newton, Lewis R. Elliott, Marta Cirach, James Grellier, Gregory N. Bratman, Mireia Gascón, Maria Luı́sa Lima, Mark Nieuwenhuijsen, Ann Ojala, Anne Roiko, Matilda van den Bosch, Lora E. Fleming

Bibliographic record

VenueHealth & Place · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
FundersLuonnonvarakeskusUniversity of British ColumbiaHorizon 2020 Framework ProgrammePlymouth UniversityChinese University of Hong KongEnvironmental Protection AgencyEconomic and Social Research CouncilUniversity of ExeterUniversity of the Sunshine CoastGriffith UniversityStanford University
KeywordsEnvironmental healthGeographyPsychologyMedicine

Abstract

fetched live from OpenAlex

Emerging evidence suggests that residential greenspace is associated with a lower prevalence of health risk behaviours, but it remains unclear whether these effects are generalizable across countries or different types of nature contact. Using representative cross-sectional samples from 18 countries/regions, we examined the associations between two types of nature contact (greenspace, nature visits), current smoking and everyday drinking. After controlling for a range of covariates, greenspace was inversely associated with current smoking and everyday drinking. Visiting natural spaces at least once a week was linked to a lower prevalence of current smoking, but unrelated to everyday drinking. Increasing residential greenspace could be a promising strategy for reducing multiple health risk behaviours, whilst visit-based interventions may be a more appropriate target for smoking cessation.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.019
GPT teacher head0.342
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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