Bibliographic record
Abstract
Evidence suggests that neighbourhood built environments and socioeconomic status are associated with health behaviours and outcomes. Sleep is a key health behaviour in the development of chronic illness. Some evidence suggests that neighbourhood characteristics are associated with differences in sleep in adult populations. The aim of this thesis was to generate quantitative evidence on the associations between neighbourhood built environment and neighbourhood socioeconomic status and sleep duration in Canadian adults. Using data from the Pathways to Health study, we estimated associations between objective measures of neighbourhood built environment (i.e., street pattern) and neighbourhood socioeconomic status and sleep duration, and odds of short or long sleep durations, in an adult sample within the city of Calgary, Alberta. We also tested if the interaction between neighbourhood street pattern and socioeconomic status was associated with differences in mean sleep duration and odds of short or long sleep. In an analysis of n=797 adults, we found that the interaction between neighbourhood street pattern and socioeconomic status was associated with sleep duration. Participants in neighbourhoods characterized by curvilinear street patterns and low socioeconomic status had the shortest marginal mean sleep at 6.93 hours per day, while those in curvilinear high socioeconomic status neighbourhoods had the longest at 7.43 hours per day. Our findings suggest that associations between neighbourhood socioeconomic status and sleep may be modified by built environment characteristics, or vice versa. Interventions to address short sleep durations should be targeted at underlying inequities in sleep between residents of neighbourhoods with different SES but should take into account neighbourhood design. Future studies should incorporate measures of both neighbourhood built environment and neighbourhood socioeconomic status and test for interactions between these neighbourhood characteristics to better understand complex pathways between neighbourhoods and sleep in adult populations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".