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Record W3006999736 · doi:10.1017/s071498082000001x

Personal and Clinical Factors Associated with Older Drivers’ Self-Awareness of Driving Performance

2020· article· en· W3006999736 on OpenAlexaff
Yu-Ting Chen, Isabelle Gélinas, Barbara Mazer, Anita Myers, Brenda Vrkljan, Sjaan Koppel, Judith Charlton, Shawn Marshall

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2020
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsMcMaster UniversityUniversity of WaterlooOttawa HospitalMcGill UniversityCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsPsychologyAffect (linguistics)ConcordanceHuman factors and ergonomicsPoison controlInjury preventionOccupational safety and healthPerceptionMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Most older adults perceive themselves as good drivers; however, their perception may not be accurate, and could negatively affect their driving safety. This study examined the accuracy of older drivers' self-awareness of driving ability in their everyday driving environment by determining the concordance between the perceived (assessed by the Perceived Driving Ability [PDA] questionnaire) and actual (assessed by electronic Driving Observation Schedule [eDOS]) driving performance. One hundred and eight older drivers (male: 67.6%; age: mean = 80.6 years, standard deviation [SD] = 4.9 years) who participated in the study were classified into three groups: underestimation (19%), accurate estimation (29%), and overestimation (53%). Using the demographic and clinical functioning information collected in the Candrive annual assessments, an ordinal regression showed that two factors were related to the accuracy of self-awareness: older drivers with better visuo-motor processing speed measured by the Trail Making Test (TMT)-A and fewer self-reported comorbid conditions tended to overestimate their driving ability, and vice versa.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.041
GPT teacher head0.303
Teacher spread0.263 · 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.

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

Citations19
Published2020
Admission routes1
Has abstractyes

Explore more

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