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Record W2810926413 · doi:10.1080/10803548.2018.1491666

A pre/post evaluation of fatigue, stress and vigilance amongst commercially licensed truck drivers performing a prolonged driving task

2018· article· en· W2810926413 on OpenAlexafffund
Michelle Cardoso, Fred Fulton, Jack P. Callaghan, Michel J. Johnson, Wayne J. Albert

Bibliographic record

VenueInternational Journal of Occupational Safety and Ergonomics · 2018
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsUniversité de MonctonUniversity of WaterlooUniversity of New Brunswick
FundersNew Brunswick Innovation Foundation
KeywordsTruckVigilance (psychology)Human factors and ergonomicsPoison controlDriving simulatorSession (web analytics)Occupational safety and healthEngineeringCognitionApplied psychologyAeronauticsSimulationPsychologyMedicineAutomotive engineeringMedical emergencyComputer science

Abstract

fetched live from OpenAlex

Purpose. The main purpose of this research study was to evaluate changes in fatigue, stress and vigilance amongst commercially licensed truck drivers involved in a prolonged driving task. The secondary purpose was to determine whether a new ergonomic seat could help reduce both physical and cognitive fatigue during a prolonged driving task. Two different truck seats were evaluated: an industrial standard seat and a new truck seat prototype. Methods. Twenty male truck drivers were recruited to attend two testing sessions, on two separate days, with each session randomized for seat design. During each session, participants performed two 10-min simulated driving tasks. Between simulated sessions, participants drove a long-haul truck for 90 min. Fatigue and stress were quantified using a series of questionnaires whereas vigilance was measured using a standardized computer test. Results. Seat interactions had a significant effect on fatigue patterns. Conclusion. The new ergonomic seat design holds potential in improving road safety and vehicle accidents due to fatigue-related accidents.

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 score0.200
Threshold uncertainty score0.589

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 integrity0.0000.000
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 head0.033
GPT teacher head0.344
Teacher spread0.311 · 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

Citations24
Published2018
Admission routes2
Has abstractyes

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