O5-2.1 Is it where you live or who you are that is important? An analysis of neighbourhood environments, self-reported physical activity and overweight / obesity in Canada's capital
Bibliographic record
Abstract
Background In Canada, there is limited research examining the effects of objectively measured neighbourhood environments on physical activity (PA) and obesity. Purpose To determine the relationships between variables from built and social environments and PA and overweight / obesity across 86 Ottawa neighbourhoods. Methods Individual-level data including self-reported leisure-time PA, height and weight were examined using a sample of 4727 adults from four combined cycles (years 2001/2003/2005/2007) of the Canadian Community Health Survey. Data on neighbourhood characteristics were obtained from the Ottawa Neighbourhood Study; a large study of neighbourhoods and health in Ottawa. Binomial multivariate multilevel models were used to examine the relationships of environmental and individual variables with PA and overweight / obesity using population weights. Results Approximately 75% of adults were inactive (<12.5 kJ/kg/day) while half were overweight / obese. Results of the multilevel models suggest that higher numbers of convenience stores and fast food outlets in a neighbourhood were associated with increased odds of being overweight / obese, while a larger number of restaurants was associated with lower odds. Season of data collection was significantly associated with PA in men and women with the odds of PA in winter being half that of summer. Intraclass coefficients were low, and identified that the models explained a small proportion of the neighbourhood-level variance in PA and overweight / obesity. Conclusions Findings from this sample identified that recreation and social environments did not exert significant influences on PA or overweight / obesity, however, food outlets did show a significant influence on female overweight / obesity. The impact of individual-level characteristics to the model was modest.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".