Travel behavior of low income older adults and development of an Accessibility calculator
Bibliographic record
Abstract
Given the aging demographic landscape, the concept of walkable neighborhoods has emerged as a topic of some interest, especially during the last decade. However, very little is known about whether neighbourhood design promotes lower income older adult's travel behaviour. Therefore, the authors i) examined the relation between trip distance and socio-demographic, and mobility related accessibility features of lower income older adults who reside in Metro Vancouver; ii) analyzed mode use behavior of lower income older adults who live in the same geographic area; and, iii) developed a web-based application to calculate the accessibility score (A-score) of lower income older adults in Metro Vancouver based on their travel behavior. The authors used multilevel linear regression to estimate the determinants of trip lengths and a multilevel multinomial model to estimate the propensity of using different travel modes. The authors found that in this lower income older population distance travelled using active modes was associated with gender (men travelled further than women, on average), living arrangement (those residing with a family member other than their spouse travelled further) and whether or not they owned a dog (interaction between dog ownership and walking for transport was positively associated with trip length). As per other reports, increased age was associated with a decreased likelihood of walking as compared to driving. Finally, among the participants all of whom were of low socioeconomic status, older adults who self-identified as a visible minority were more likely to walk than use their car. Taken together these findings lend credence to the role of neighbourhood design and the opportunity landscape (neighbourhood accessibility) to promote active modes of transport in lower income older adults. Therefore, the authors created a web-based calculator that generates an Accessibility (A)- score using Google Maps API v3 that can be used to evaluate neighbourhoods as to their livability for older adults.
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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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".