SAFETY OF PUBLIC BUSES IN NATURALLY OCCURRING RETIREMENT COMMUNITIES
Bibliographic record
Abstract
Abstract Community mobility is critical to support aging-in-place. For older adults living in low-and moderate-income urban communities, driving may not be an option due to costs associated with car ownership, impracticalities of cars in urban settings, or a lack of driving history. Some older adults must retire from driving due to changes in their functional status. Safe public transportation is therefore paramount to equity in community mobility. The purpose of this Ecological theory informed study was to explore supports and limitations regarding safe public bus use in naturally occurring retirement communities (NORC). A multiple qualitative case study design was used with purposive sampling of representative cases, including four bus routes in NORC neighborhoods, two in Toronto, Canada, and two in NYC. Field observations and in-depth semi-structed interviews were conducted. Inclusion criteria for participants were: 1) Age 60 and older 2) Resident of one of the four NORC neighborhoods 3) Ride the bus on average 2 or more times per month 4) Ambulatory with or without a walking aid (e.g. cane, rollator) and 5) English speaking. A descriptive, thematic approach was used for data coding and analyses, and data was organized according to the Framework Method. Approximately 45 hours of field observations were conducted, and N=17 NORC residents participated in interviews. Results revealed four major themes: Impact of other people’s behavior, Problematic passageways: obstructions, Personal impact on safety, and Space & time orientation. Several subthemes also emerged. Results suggest implications for policy and education to increase safety for older adult bus users.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".