How much is enough? Assessing the influence of neighborhood walkability on undertaking 10-minute walks
Bibliographic record
Abstract
Neighborhood walkability is increasingly perceived as an effective way to support individuals’ health, since living in a walkable environment is associated with increases in utilitarian walking. Yet, while people are more likely to walk in more walkable neighborhoods, increased walkability can also lead to walking shorter distances, thus mitigating the positive health outcomes associated with walkable environments. Given that the World Health Organization recommends physical activity to be performed in sessions of at least 10 minutes, the aim of this research is to explore the relationship between neighborhood walkability and individuals’ likeliness of walking in sessions of at least 10 minutes. A multilevel logistic regression is generated using data from the Montreal, Canada, 2013 Origin-Destination Survey. The results show that the probability of walking at least 10 minutes for shopping purposes is equally high in the 80-89 and 90-100 Walk Score neighborhoods. In contrast, car ownership is a strong predictor of walking at least 10 minutes, especially in higher Walk Score neighborhoods. These findings suggest that transport policies aimed at reducing car ownership, combined with land use policies, can be most effective in supporting the minimal 10-minute sessions of walking for shopping purposes. This study provides a nuanced assessment of walkability and is of relevance to researchers and planners wishing to assess and develop policies for increasing health benefits through active transportation.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".