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Record W2014214533 · doi:10.1901/jaba.2010.43-369

INCREASING SEAT BELT USE IN SERVICE VEHICLE DRIVERS WITH A GEARSHIFT DELAY

2010· article· en· W2014214533 on OpenAlexaboutno aff
Ron Van Houten, J. E. Louis Malenfant, Ian J. Reagan, Kathy J. Sifrit, Richard Compton, Jeff Tenenbaum

Bibliographic record

VenueJournal of Applied Behavior Analysis · 2010
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsnot available
FundersU.S. Department of Transportation
KeywordsSeat beltTruckSignificant differencePoison controlAutomotive engineeringDuration (music)Variable (mathematics)AeronauticsEngineeringTransport engineeringSimulationMathematicsMedicineMedical emergencyStatisticsPhysics

Abstract

fetched live from OpenAlex

This study evaluated a device that prevents drivers from shifting vehicles into gear for up to 8 s unless seat belts are buckled. Participants were 101 commercial drivers who operated vans, pickups, or other light trucks from the U.S. and Canada. The driver could escape or avoid the delay by fastening his or her seat belt before shifting out of park. Unbelted participants experienced either a constant delay (8 s) or a variable delay (M = 8 s). A 16-s delay was introduced for those U.S. drivers who did not show significant improvement. Seat belt use increased from 48% to 67% (a 40% increase) for U.S. drivers and from 54% to 74% (a 37% increase) for Canadian drivers. The fixed delay was more effective for U.S. drivers than the variable delay, but there was no difference between these two delay schedules for Canadian drivers. After the driver fastened his or her seat belt, it tended to remain fastened for the duration of the trip.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.020
GPT teacher head0.320
Teacher spread0.301 · 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

Citations22
Published2010
Admission routes1
Has abstractyes

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