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A Meta-Analysis of Driving Performance and Crash Risk Associated with the Use of Cellular Telephones While Driving

2005· article· en· W586620715 on OpenAlexaff
Jeff K. Caird, Chip T Scialfa, Geoff Ho, Alison Smiley

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPhoneCrashHuman factors and ergonomicsPoison controlComputer scienceComputer securityMedicineEnvironmental health

Abstract

fetched live from OpenAlex

This paper addresses the effects of cell phones on driving by means of a review of the literature and an analysis of scientifically credible epidemiological and driver performance studies. A total of 84 articles were obtained covering the period from 1969 to 2004. Sixty-eight articles were research papers measuring driving performance while using a cell phone and 16 articles were epidemiological studies that examined cell phone usage and their relationship to vehicular crashes. Epidemiological findings consistently showed an increase in crashes associated with use of cell phones. However, these studies did not control for exposure to cell phone use or to driving. The negative impact of cell phone usage is larger for responses to critical events than for vehicular control. Drivers responded about a quarter of a second later to stimuli in the presence of a cell phone distractor for all studies that were analyzed. Hands-free cell phones produced similar performance decrements to hand-held phones.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.000

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 head0.305
Teacher spread0.220 · 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

Citations11
Published2005
Admission routes1
Has abstractyes

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