Walking to work in Canada: health benefits, socio-economic characteristics and urban-regional variations
Bibliographic record
Abstract
BACKGROUND: There is mounting concern over increasing rates of physical inactivity and overweight/obesity among children and adult in Canada. There is a clear link between the amount of walking a person does and his or her health. The purpose of this paper is to assess the health factors, socio-economic characteristics and urban-regional variations of walking to work among adults in Canada. METHODS: Data is drawn from two cycles of the Canadian Community Health Survey: 2001 and 2005. The study population is divided into three groups: non-walkers, lower-duration walkers and high-duration walkers. Logistic regression modeling tests the association between levels of walking and health related outcomes (diabetes, high blood pressure, stress, BMI, physical activity), socio-economic characteristics (sex, age, income, education) and place of residence (selected Census Metropolitan Areas). RESULTS: In 2005, the presence of diabetes and high blood pressure was not associated with any form of walking. Adults within the normal weight range were more likely to be high-duration walkers. Females and younger people were more likely to be lower-duration walkers but less likely to be high-duration walkers. There was a strong association between SES (particularly relative disadvantage) and walking to work. In both 2001 and 2005, the conditions influencing walking to work were especially prevalent in Canada's largest city, Toronto, as well as in several small to medium sized urban areas including Halifax, Kingston, Hamilton, Regina, Calgary and Victoria. CONCLUSION: A number of strategies can be followed to increase levels of walking in Canada. It is clear that for many people walking to work is not possible. However, strategies can be developed to encourage adults to incorporate walking into their daily work and commuting routines. These include mass transit walking and workplace walking programs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".