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Record W2766725579 · doi:10.1177/0733464817739154

Transportation and Aging: An Updated Research Agenda for Advancing Safe Mobility

2017· review· en· W2766725579 on OpenAlexaff
Anne E. Dickerson, Lisa J. Molnar, Michel Bédard, David W. Eby, Sherrilene Classen, Janice M. Polgar

Bibliographic record

VenueJournal of Applied Gerontology · 2017
Typereview
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsWestern UniversityLakehead University
Fundersnot available
KeywordsPsychological interventionVariety (cybernetics)Multidisciplinary approachGerontologyOlder peopleScale (ratio)PsychologyMedical educationMedicineNursingSociologyComputer science

Abstract

fetched live from OpenAlex

This article discusses what is currently known about three important topics related to older driver safety and mobility: screening and evaluation, education and training interventions, and in-vehicle technology. Progress is being made to improve the safe mobility of older adults in these key areas; however, significant research gaps remain. This article advances the state of knowledge by identifying these gaps, and proposing further research topics will improve the lives of older adults. In addition, we discuss several themes that emerged from the review, including the need for multidisciplinary, community-wide solutions; large-scale, longitudinal studies; improved education/training for both older adults themselves and the variety of stakeholders involved in older adult transportation; and programs and interventions that are flexible and responsive to individual needs and differences.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.006
Science and technology studies0.0020.002
Scholarly communication0.0060.011
Open science0.0020.004
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0080.002

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 head0.466
GPT teacher head0.604
Teacher spread0.138 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations61
Published2017
Admission routes1
Has abstractyes

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