Influence of Driving and Transportation Access on Social Isolation Risk Among Older Adults
Bibliographic record
Abstract
Abstract Background. Transportation is essential to accessing healthcare and community services, but the inability to find transportation may hinder social interactions and connectivity. This study examined driving and transportation access associated with self-reported social isolation risk among adults age 60 years and older. Methods. The Upstream Social Isolation Risk Screener (U-SIRS) was developed to assess social isolation risk among older adults within clinical and community settings. Comprised of 13 items (Cronbach’s alpha=0.80), the U-SIRS assesses physical, emotional, and social support aspects of social isolation. Using an internet-delivered survey, data were analyzed from a national sample of 4,082 adults age 60 years and older. Theta scores for the U-SIRS served as the dependent variable, which were generated using Item Response Theory. An ordinary least squares regression model was fitted to identify transportation-related indicators associated with social isolation risk. Results. Approximately 13% of participants did not drive and 18.2% reported not being able to identify a ride or transportation when needed. Higher U-SIRS scores were reported among participants who did not drive (B=0.034, P=0.020). Lower U-SIRS scores were reported among those who live with a spouse/partner (B=-0.153, P<0.001) and those who reported the ability to get a ride from a family member (B=-0.160, P<0.001), friend (B=-0.256, P<0.001), or taxi (B=-0.032, P=0.044). Every additional source of transportation available significantly reduced participants’ U-SIRS score (B=-0.239, P<0.001). Conclusion. Given transportation options may reflect physical functioning, social networks, and socioeconomic status, study findings suggest transportation access is an important contextual factor associated with social isolation risk.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".