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Record W1489203962

Addressing Elderly Mobility Issues in Wisconsin

2011· article· en· W1489203962 on OpenAlexaboutno aff
Jason Bittner, Patrick Fuchs, Tim Baird, Adam Smith

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationGerontologyQuarter (Canadian coin)Social isolationTRIPS architectureCrashBusinessMedicinePsychologyEnvironmental healthGeographyEngineeringTransport engineering
DOInot available

Abstract

fetched live from OpenAlex

The aging of baby boomers poses significant challenges to Wisconsin’s existing transportation infrastructure and specialized transit programs. From 2010 to 2035, the number of elderly Wisconsinites is projected to grow by 90 percent, an increase of 702,760 persons. By 2035, residents age 65 and over will comprise nearly a quarter of the population of Wisconsin, as every county in the state will experience growth in the elderly share of their population over the next 25 years. The U.S. Department of Transportation’s 2003 National Household Travel Survey found that personally-owned vehicles account for over 90 percent of trips taken by elderly residents; the extrapolation of this data suggests an overwhelming majority of Wisconsin’s future elderly residents will be accustomed to driving. Because elderly persons are vulnerable to a decline in visual, cognitive, and psychomotor skills, a dramatic increase in the number of elderly drivers has serious safety implications for the state. Elderly drivers are more likely to have crashes on a per-mile basis, more likely to be at fault in a multicar crash, and more likely to be killed or injured than are younger people in a crash of comparable magnitude. When elderly drivers are forced to stop driving or self-regulate in response to declining abilities and safety concerns, they face increased isolation from social, family, and civic activities and decreased access to medical services. These safety and social ramifications demand an examination of the state’s current driver licensing and education practices, infrastructure design protocols, and specialized and public transit efforts. This report provides analysis of Wisconsin’s existing services, coordinated by the DOT and other State agencies, collects information from elderly residents, and reviews national and international best practices to allow the Wisconsin Department of Transportation (WisDOT) to better manage approaching demographic challenges. Recommendations are provided that include changes in internal structure to address older residents’ mobility concerns, education and outreach opportunities, and development of incentives to provide off prime hour services.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.096
GPT teacher head0.292
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2011
Admission routes1
Has abstractyes

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