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Record W2136943711 · doi:10.5014/ajot.64.2.215

Seniors’ Perceptions of Vehicle Safety Risks and Needs

2010· article· en· W2136943711 on OpenAlexaffabout
Lynn Shaw, Jan Miller Polgar, Brenda Vrkljan, Jill A. Jacobson

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2010
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsMcMaster UniversityWestern University
Fundersnot available
KeywordsSAFERFocus groupExcellenceOccupational safety and healthQualitative researchPoison controlPerceptionHuman factors and ergonomicsTransport engineeringApplied psychologyEngineeringBusinessPsychologyMedicineMedical emergencyComputer securityMarketingComputer science

Abstract

fetched live from OpenAlex

The investigation of vehicle safety needs for older drivers and passengers is integral for their safe transportation. A program of research on safe transportation for seniors was launched through AUTO21, a Canadian Network of Centres of Excellence. This national research network focuses on a wide range of automotive issues, from materials and design to safety and societal issues. An inductive qualitative inquiry of seniors' driving experiences, safety feature use, and strategies to prevent injury and manage risks was a first step in this program. We conducted interviews and focus groups with 58 seniors without disabilities and 9 seniors with disabilities. We identified a lack of congruity between the vehicle and safety feature design and seniors' needs. Seniors described strategies to manage their safety and that of others. Specific aspects of vehicle design, safety features, and action strategies that support safer use and operation of a vehicle by seniors are outlined.

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 score0.029
Threshold uncertainty score0.392

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 studies0.0000.000
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 head0.085
GPT teacher head0.457
Teacher spread0.373 · 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

Citations20
Published2010
Admission routes2
Has abstractyes

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