A comprehensive evaluation of built-environment as a risk factor for sleep disruption
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background/ Aim Sleep disruption is a significant public health issue, given its high prevalence and links to both injury and chronic disease. Interventions for improving sleep often focus on individual-level behaviour change. Modifying aspects of the built environment may be a strategy for population-level improvements. Few studies have evaluated the impacts of built environment on sleep. We assessed relationships between the built environment and sleep disruption using a well-characterized, population-based cohort. Methods Analyses were conducted among participants of the British Columbia Generations Project (BCGP) with complete data on built environment factors and self-reported sleep duration and quality(n=23,556). Measures of air pollution (PM₂.₅, NO₂), greenness (density within 250-metres) and intensity of light-at-night (LAN) were obtained from the Canadian Urban Environmental Research Consortium (CANUE), and linked to participants residential postal codes. Logistic regression analysis, adjusted for age and sex, was used to estimate the association between each built environment factor and self-reported sleep duration (<7 hours, ≥7 hours) and difficulty in falling or staying asleep (sometimes/most of the time/ always vs. rarely/never). Results Increased PM₂.₅ was associated with lower odds of insufficient sleep duration (OR=0.85/5µg/m³; 0.74-0.97) and greater odds of difficulty falling/staying asleep (OR 1.54/5µg/m³; 1.37-1.74). Increased LAN intensity was associated with greater odds of insufficient sleep (OR=1.04/10-unit; 1.02-1.07) but not with difficulty falling/staying asleep. Greenness exposure in the top quartile was associated with reduced odds of insufficient sleep (OR=0.92; 0.86-0.99) and difficulty staying/falling asleep (OR=0.97; 0.95-0.99) compared to those in the bottom quartile. Greenness, LAN and PM₂.₅ were moderately correlated (-0.5 < r < 0.5). Conclusions BCGP’s rich data enabled a comprehensive evaluation of the built-environment as a modifiable determinant of sleep disruption. Further analyses will elucidate the mediating effects of sleep on the links between built-environment and chronic disease. Keywords Sleep; Built Environment; Air Pollution; Greenness; Light pollution
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it