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Field Quantification of Physical Exposures of Police Officers in Vehicle Operation

2011· article· en· W2157412359 on OpenAlexaff
Colin D. McKinnon, Jack P. Callaghan, Clark R. Dickerson

Bibliographic record

VenueInternational Journal of Occupational Safety and Ergonomics · 2011
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWork (physics)Human factors and ergonomicsIntervention (counseling)OfficerPoison controlOccupational safety and healthEngineeringDocumentationPsychological interventionWork shiftData collectionTransport engineeringApplied psychologyMedical emergencyComputer sciencePsychologyMedicineOperations managementNursing

Abstract

fetched live from OpenAlex

Mobile police officers perform many of their daily duties in their vehicles. Combined workspace inflexibility and prolonged driving create potential musculoskeletal injury risks. Limited research exists that quantitatively describes postural and load exposures associated with mobile police work. The purpose of this study was to characterize officer activity during a typical workday and identify opportunities for ergonomic intervention. Digital video of traffic officers (N = 10) was used to classify postures according to work activity. Cumulative time in 10 activities was calculated, and a time-history of driver activity documented. Most (55.5 ± 13.4%) time was out of the vehicle, and 22.3 ± 10.5% was spent in single-arm driving. On paper documentation and mobile data terminal use were identified as in-car activities that may benefit from targeted interventions. The primary contribution of this study is characterization of daily mobile police activity and the identification of possible intervention strategies to mitigate physical exposure levels.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score0.294

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)0.0000.000

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 head0.038
GPT teacher head0.337
Teacher spread0.300 · 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

Citations36
Published2011
Admission routes1
Has abstractyes

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