Mechanisms underlying the associations between different types of nature exposure and sleep duration: An 18-country analysis
Bibliographic record
Abstract
Whilst green space has been linked to healthier sleep outcomes, the roles of specific types of nature exposure, potential underlying mechanisms, and between-country variations in nature-sleep associations have received little attention. Drawing on cross-sectional survey data from an 18-country sample of adults (N = 16,077) the current study examined: 1) the relative associations between six different types of nature exposure (streetscape greenery, blue view from home, green space within 1 km, coast within 1 km, green space visits, blue space visits) and insufficient sleep (<6 h vs. 7-10 h per day); 2) whether these relationships were mediated by better mental wellbeing and/or physical activity; and 3) the consistency of these pathways among the different countries. After controlling for covariates, neighbourhood nature measures (green space, coast within 1 km) were not significantly associated with insufficient sleep; but nature visible from home (streetscape greenery, blue views) and recreational visits to green and blue spaces were each associated with less insufficient sleep. Significant nature-sleep associations were mediated, to varying degrees, by better mental wellbeing, but not self-reported physical activity. Country-level heterogeneity in the strength of nature-sleep associations was observed. Increasing nature visible from the home may represent a promising strategy for promoting healthier sleep duration at the population level, whilst nature-based interventions encouraging individuals to spend time in local green/blue spaces may be an appropriate target to assist individuals affected by insufficient sleep.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".