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Record W2954102699 · doi:10.1017/s0714980819000394

Speeding and Speed Modification of Older Drivers: Does Vehicle Type Make a Difference?

2019· article· en· W2954102699 on OpenAlexafffundabout
A Cull, Michelle M. Porter, Satoru Nakagawa, Glenys Smith, Mark Rapoport, Shawn Marshall, Michel Bédard, Holly Tuokko, Brenda Vrkljan, Gary Naglie, Anita M. Myers, Barbara Mazer

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2019
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsCentre for Interdisciplinary Research in RehabilitationUniversity of WaterlooToronto Rehabilitation InstituteMcMaster UniversityUniversity of VictoriaUniversity of ManitobaUniversity Health NetworkOttawa HospitalLakehead UniversityBaycrest HospitalMcGill UniversityMcGill University Health CentreUniversity of OttawaUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsTruckSpeed limitVehicle typeAutomotive engineeringAccelerationLongitudinal studyCommercial vehiclePoison controlTransport engineeringPsychologyAeronauticsEngineeringMedicineEnvironmental healthMathematicsStatistics

Abstract

fetched live from OpenAlex

The purpose of this study was to examine whether vehicle type based on size (car vs. other = truck/van/SUV) had an impact on the speeding, acceleration, and braking patterns of older male and female drivers (70 years and older) from a Canadian longitudinal study. The primary hypothesis was that older adults driving larger vehicles (e.g., trucks, SUVs, or vans) would be more likely to speed than those driving cars. Participants (n = 493) had a device installed in their vehicles that recorded their everyday driving. The findings suggest that the type of vehicle driven had little or no impact on per cent of time speeding or on the braking and accelerating patterns of older drivers. Given that the propensity for exceeding the speed limit was high among these older drivers, regardless of vehicle type, future research should examine what effect this behaviour has on older-driver road safety.

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)0.001
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 score0.329
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.290
Teacher spread0.265 · 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

Citations6
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicOlder Adults Driving StudiesFrench-language works237,207