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The Impact of Mobile Data Terminal Use on Posture and Low-Back Discomfort When Combined With Simulated Prolonged Driving in Police Cruisers

2013· article· en· W1993272674 on OpenAlexaff
Kristina M. Gruevski, Colin D. McKinnon, Clark R. Dickerson, Jack P. Callaghan

Bibliographic record

VenueInternational Journal of Occupational Safety and Ergonomics · 2013
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPelvisPhysical medicine and rehabilitationLumbarWork (physics)Terminal (telecommunication)Lumbar spineTask (project management)MedicineSimulationPhysical therapyComputer scienceEngineeringSurgery

Abstract

fetched live from OpenAlex

The introduction of mobile computing within a constrained vehicle environment has led to changes in the task demands of occupational groups such as professional drivers and law enforcement officers. The purpose of this study was to examine how mobile data terminal (MDT) use interacts with prolonged driving to induce postural changes or low-back discomfort. Eighteen participants (9 male, 9 female) completed two 120-min simulated driving sessions. Time-varying lumbar spine and pelvis postures, seat pan interface pressures and ratings of perceived discomfort were recorded at 15-min intervals. The introduction of a computer interface decreased pelvic posterior rotation by an average of 15° with respect to upright standing and increased peak average discomfort in the neck (5.9 mm), left shoulder (6.8 mm), midback (10.9 mm), low back (10.6 mm) and pelvis (11.5 mm) compared to driving alone. The incorporation of mobile computing warrants consideration in the design of vehicle work environments.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.388

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 communication0.0000.001
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 head0.022
GPT teacher head0.335
Teacher spread0.313 · 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

Citations23
Published2013
Admission routes1
Has abstractyes

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