Examining the influence of built environment on sleep disruption
Bibliographic record
Abstract
Background: Modifying aspects of the built environment may be an effective strategy for population-level improvements to sleep. However, few comprehensive evaluations of built environment and sleep have been completed. Methods: We conducted a cross-sectional study among participants of the British Columbia Generations Project (BCGP) who self-reported sleep duration (n = 28,385). Geospatial measures of light-at-night (LAN), greenness, air pollution (PM 2.5 , NO 2 , SO 2 ), and road proximity were linked to participant baseline residential postal codes. Logistic regression models, adjusted for age and sex, were used to estimate the association between these factors and self-reported sleep duration (<7 vs. ≥7 hours). Results: Interquartile range (IQR) increases in LAN intensity, greenness, and SO 2 were associated with 1.04-fold increased (95% CI = 1.02, 1.07), 0.95-fold decreased (95% CI = 0.91, 0.98), and 1.07-fold increased (95% CI = 1.03, 1.11) odds, respectively, of reporting insufficient sleep (i.e., <7 hours per night). Living <100 m from a main roadway was associated with a 1.09-fold greater odds of insufficient sleep (95% CI = 1.02, 1.17). Results were unchanged when examining all factors together within a single regression model. In stratified analyses, associations with SO 2 were stronger among those with lower reported annual household incomes and those living in more urban areas. Conclusions: BCGP’s rich data enabled a comprehensive evaluation of the built environment, revealing multiple factors as potentially modifiable determinants of sleep disruption. In addition to longitudinal evaluations, future studies should pay careful attention to the role of social disparities in sleep health.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".