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Record W2573032442

Teen Drivers' Perceptions of Their Peer Passengers: Qualitative Study

2015· article· en· W2573032442 on OpenAlexaff
Johnathon P. Ehsani, Denise L. Haynie, Christina Luthers, Jessamyn G. Perlus, E.P. Gerber, Marie Claude Ouimet, Sheila G. Klauer, Bruce G. Simons‐Morton

Bibliographic record

VenueTransportation Research Board 94th Annual Meeting · 2015
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsDistractionSAFERPerceptionPsychologyGrounded theoryRisk perceptionQualitative researchCrashApplied psychologyHuman factors and ergonomicsPoison controlSocial psychologyComputer securityMedicineEnvironmental healthComputer scienceSociology
DOInot available

Abstract

fetched live from OpenAlex

The presence of peer passengers increases the risk of fatal crashes among teenage drivers. Distraction and social influence are the two main factors associated with this increased risk. Teen drivers’ perceptions of their peer passengers with regard to distraction and social influence could help inform the understanding of the conditions under which peer passengers increase crash risk or promote safer driving. The purpose of this study was to examine such perceptions. A convenience sample of male and female drivers participated in a semistructured interview process that included questions about their perceptions of the effects of peer passengers on driving. The analysis of the interviews was guided by a grounded theory approach. Teenage drivers were found to be aware of the risk that peer passengers posed. Some described having passengers in the vehicle as distracting and recognized that the level of distraction increased with the number of passengers. Drivers who felt responsible for the safety of their p...

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)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.060
GPT teacher head0.369
Teacher spread0.309 · 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 designQualitative
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

Citations0
Published2015
Admission routes1
Has abstractyes

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Same venueTransportation Research Board 94th Annual MeetingSame topicTraffic and Road SafetyFrench-language works237,207