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Record W2748544967 · doi:10.1177/1460408617724816

Driving habits – A Canadian study

2017· article· en· W2748544967 on OpenAlexaboutno aff
Shayesteh Jahanfar

Bibliographic record

VenueTrauma · 2017
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsnot available
Fundersnot available
KeywordsSeat beltMedicineInjury preventionEnvironmental healthOccupational safety and healthHuman factors and ergonomicsPoison controlSafe drivingSuicide preventionPopulationEthnic groupMedical emergencyEngineeringAutomotive engineering

Abstract

fetched live from OpenAlex

Introduction Motor vehicle accidents are a significant cause of morbidity and mortality. Safe driving behavior constitutes proper use of seat belts as a driver and as a passenger. The correct use of seat belts has been shown to reduce death and injury following motor vehicle accidents by more than 50%. This study aims at investigating seat belt use and driving habits among Canadians. Method A population-based database from the Canadian Community Health Survey was analyzed. Result About 12% reported that they use seat belts most of the times, rarely or never and 27% of respondents were using cellphone while driving often or sometimes; 8% of respondents admitted to driving after 2 or more alcoholic drinks. Seat belt use in taxi passengers is much lower than in own cars, with 40% not using a seatbelt all of the time when in a taxi. Discussion and conclusions The major risk factors for not wearing seat belt as a passenger include age, education, ethnicity and income.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.015
GPT teacher head0.228
Teacher spread0.213 · 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 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
Published2017
Admission routes1
Has abstractyes

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