Moving around a Large City in Latin America: The Mobility Challenges Faced by Older Adults with Disabilities
Bibliographic record
Abstract
A growing body of research has shown that barriers in the urban environment can be disabling by reducing the ability of older people to manage independently in the community, but also because they can negatively affect health by limiting the possibilities to move outside the home. In this study, we ask how obstacles in the urban environment are associated with the need for help to go to places in the community. To respond to this question, we used the Annual Household Survey of the City of Buenos Aires, Argentina 2018, which had a specific questionnaire for people with disabilities. From this sample, we selected adults aged 65 years or older with difficulties in at least one of six domains: vision; hearing; upper and lower body mobility; cognition; self-care; and communication. The final sample consisted of 513 persons (weighted = 109,316). First, we conducted a principal component analysis identifying three factors from variables of obstacles to access and use the urban environment: transportation; outdoor spaces; and information. Second, through a logistic regression model, we observed a direct relationship between these factors and the need for help to move in the community, controlling for sociodemographic characteristics, health status, and number of disabilities. This paper provides evidence on the significance of improving urban spaces to reduce dependent mobility. In Latin America, cities still face many challenges in becoming more age-friendly.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".