Associations between neighbourhood street pattern, neighbourhood socioeconomic status and sleep in adults
Bibliographic record
Abstract
Sleep duration is a risk factor for poor health and all-cause mortality. Evidence suggests that neighbourhood characteristics such as built environment and socioeconomic status (SES) may affect sleep duration in adults. This study examined the relationship between neighbourhood built environment (i.e., measured via the street pattern) and SES with sleep duration in adults (n = 797) from 12 neighbourhoods in Calgary (Canada). Covariate adjusted linear and multinomial logistic regression models estimated the associations between street pattern (grid, warped-grid, curvilinear), SES and sleep duration. We also tested if the interaction between street pattern and SES was associated with sleep duration. Although neighbourhood street pattern and neighbourhood SES were not independently associated with sleep, the interaction between street pattern and neighbourhood SES, was associated with mean sleep duration. Individuals living in curvilinear low SES neighbourhoods had the shortest sleep duration (6.93 h per day; 95% CI 6.68, 7.18), while those living in curvilinear high SES neighbourhoods slept the longest (7.43 h per day; 95% CI 7.29, 7.57). Neighbourhood street pattern and SES, as well as their interaction, were not associated with the odds of sleeping shorter or longer than 7 to 8 h per day. Our findings suggest that the combined effect of the neighbourhood built environment and SES is potentially important for influencing sleep duration. More research is needed to understand the complex interrelationships between the built environment, SES, and sleep.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 teacher head, 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".